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BUGSPharmaSUG 2025 Papers - Index
256 papers. Source: PharmaSUG (Pharmaceutical Software Users Group) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | A Case Study on Visualization Methods in R | Margaret Huang, Chunting Zheng, Lei Zhao, Todd Case |
The R programming language [R] provides some basic packages with essential functions which allow us to quickly create simple plots such as scatter plots, box plots, and histograms. In addition to the generic plotting functions, R frequently offers many built-in libraries and updates that meet our needs, including creating different types of plots, improving appearance, or even making them interactive. This paper is based on a summer intern project and illustrates how to efficiently generate various high-quality figures for clinical trial data visualization in R | box plot, histogram, washout period | source | 15 |
| 2 | A codelist generator for define.xml using a SAS Studio macro and RStudio function | Valerie Cadorett | A define.xml codelist should contain all possible terminology for a variable, as specified on CRF, not just the terms present in a dataset. The Import Validation Metadata feature in Pinnacle 21 Enterprise (P21E) has two options for updating a define.xml: merging changes or overwriting it completely. Merging changes retains all metadata but does not create codelists for variables with more than 30 unique terms and does not remove unused terms. Overwriting the define.xml keeps only the terms present in the dataset and removes the other terms. The objective of this paper is to share a SAS macro and R function, referred to in this paper as the Codelist Generator or generator, designed to aid reconciliation and validation of codelists and generate define.xml codelists based on study data. The generator addresses limitations in P21E when updating codelists through the Import Validation Metadata feature. Key benefits include: * Creating codelists for variables with more than 30 unique terms | data cleaning, sas output, macro definition, cdisc | source | 7 |
| 3 | A collaborative and agile approach for end to end Standards Governance and Release | Kairav Tarmaster, Pratiksha Wani |
The Data Standards team manages and governs individual data models such as Data Collection (CRF, DTS), SDTM, and ADaM for an organization. When an organization sees each data model as responsibility of a separate business function, it results in disconnected (unmapped) and unharmonized standards. Like a symphony, the Data Standards team should be a single unit bringing different experiences and expertise with a common goal, i.e., release standards that are mapped (connected), consistent, and error-free to enable faster study builds with improved quality. While defining the process and procedures, it is important to understand the various types of requests received by the standards team and their scope, define the roles and responsibilities of its people, and identify the touchpoints and requirements from a Metadata Repository solution for managing, governing, and releasing standards | proc freq, edc, excel, cdisc | source | 10 |
| 4 | A Customizable Framework in R for Presenting BICR Data in a User-Friendly Format | Reneta Hermiz, Jing Ji |
In oncology trials, Blinded Independent Central Review (BICR) data plays a crucial role in providing unbiased and standardized assessments of imaging data. BICR data ensures the consistency and reliability of imaging assessments compared to investigator assessments. This independent review process contributes to the robustness and quality control of trial outcomes. BICR data, although crucial, can be complex and fragmented across multiple SDTM domains and can pose significant challenges for researchers and clinicians. This paper outlines a process in RStudio for generating a centralized dataset that contains records from multiple SDTM domains. Then, we utilize DT (0.33.3; Xie Y, 2025) and shiny (1.10.0.9000; Chang W, 2025) RStudio packages to produce a web- based application with drillable tables according to three hierarchical levels: lesion level, visit level, and subject level. By linking all tables to a centralized dataset, our approach provides a tool to simplify BICR data review | percent change from baseline, change from baseline, disease response, data review, missing value, data science, raw data, cdisc | source | 14 |
| 5 | A discussion of the new ADaM guidance (ADNCA and ADPPK) for pharmacokinetics. | Luke Reinbolt | Pharmacokinetics (PK) is the study of the effect of the body on a drug. Different mathematical methods are used to calculate PK parameters that describe, characterize, and quantify this effect. Two common ways to determine PK parameters are non-compartmental analysis (NCA) and population PK (PopPK). Non-compartmental analysis (NCA) is one class of mathematical methods for studying the level of exposure following administration of a drug and is commonly used to analyze serial drug concentration data from clinical trials by individual subjects. Population PK (PopPK) is a model-based representation of PK processes with a statistical component, enabling identification of the sources of inter- and intraindividual variability. Recent guidance has been published by CDISC which directly affects the PK process. The two guidances are the Analysis Data Model Implementation Guide for Non-compartmental Analysis Input Data (ADNCA) and Basic Data Structure for ADaM PopPK Implementation Guide (ADPPK) | key, character variable, pk parameter, compartmental analysis, numeric variable, adpp, population pk, cdisc | source | 3 |
| 6 | A Game Changer for Efficient SAS Programming using ChatGPT | Jyoti (Jo) Agarwal | This paper explores the potential of integrating ChatGPT, a natural language processing model developed by OpenAI, into SAS programming within the pharmaceutical industry. Leveraging ChatGPT's capabilities can enhance productivity by assisting with code generation, debugging, and workflow optimization. By demonstrating practical applications and techniques, this paper illustrat es how ChatGPT can streamline statistical programming, reduce errors, and empower programmers with innovative tools to tackle common challenges | sas programming, pharmaceutical industry | source | 11 |
| 7 | A Gentle Introduction to creating graphs in Python, R and SAS | Dane A. Korver | If you have read my previous papers, I have used Wordle and temperature data to teach us how to create various graphs in SAS and R. This paper will create a bar and line chart using a very small dataset to help us grow our abilities in graphing our data using Python, R and SAS to help us visualize trends over time | bar chart, line chart, proc step, proc format, proc sgplot | source | 9 |
| 8 | A Little Bit of This and That: Use cases, implementation, and documentation when using multiple CDISC standards, CTs, and regulatory guidances in an SDTM study | Charity Quick | In the past, organizations typically selected and adhered to a single version of the SDTM Implementation Guide and CDISC CT when creating tabulation data for a study. However, the landscape of clinical data standards has evolved significantly with the introduction of Therapeutic Area User Guides (TAUGs), QRS supplements, and regulatory recommendations like "Submitting Patient-Reported Outcome Data in Cancer Clinical Trials". Additionally, frequent updates to the FDA "Study Data Technical Conformance Guide" may contain agency recommendations that have not found their way into CDISC standards that are updated less frequently. This evolution has led to the increasingly common and encouraged practice of implementing multiple CDISC standards and regulatory guidances within a single study. The adoption of Define-xml 2.1 has provided a robust framework for documenting the use of multiple Implementation Guides and standards in the submission required define.xml file | cdisc controlled terminology, data cleaning, cdisc | source | 14 |
| 9 | A Macro to Automate Data Visualization using SAS Graph Template Language (GTL) | Ming Yang | When analyzing clinical trial data, we often engage in data exploration to identify trends, detect outliers and compare various subgroups. These exploratory analyses must be conducted quickly while maintaining quality and relevance. This process may involve deriving new variables and presenting the data in a clear and meaningful way. The paper introduces a powerful SAS macro that facilitates data exploration in the pharmaceutical industry by supporting three mostly used figure types: line plots, spaghetti plots and box plots. This macro includes input parameters that controls figure types, formats, subgroups and various other customizations. While a few input parameters are required to produce results, most have default values. By simply replacing the parameters, the macro can generate multiple figures with the desired formats and layouts. It supports unlimited types of analysis data, such as laboratory results, vital signs or questionnaire responses | graph template language, box plot | source | 4 |
| 10 | A Macro to Automatically Check SAS Logs for Common Issues | Hailin Yu | Manually reviewing SAS logs can be time-consuming and error-prone, especially in complex clinical trial programming. This paper presents a macro that automatically scans SAS logs for common issues like errors, warnings, unresolved macro variables, and MERGE statements with missing BY variables. By automating log checking, programmers can save time and ensure high-quality outputs |
sas log, %if-%then-%else, macro variable | source | 5 |
| 11 | A Method to Add Additional Necessary Datasets to Existing Define.XML | Jeff Xia, Sandeep Meesala |
Define-XML is required by global health agencies for every study included in each electronic submission to inform regulators about the datasets, variables, controlled terms, and other specified metadata utilized. Once a define-XML file is created, there are scenarios that necessitate updating the existing file to add additional datasets. For instance, ADBASE might be necessary to augment baseline characteristics in response to requests for information from agencies post-initial or the inclusion of pharmacokinetic / pharmacodynamic (PK/PD)related ADaM datasets may be included for specific country requests or based on study specific requirement. The manual creation and revision of define-XML files can be time- consuming and prone to errors, particularly when managing multiple datasets | excel, dataset, %if-%then-%else, cdisc | source | 5 |
| 12 | A New Gateway to Open Source in SAS Viya | Jim Box, Mary Dolegowski |
PROC GATEWAY is a new procedure in SAS Viya that allows you to run code written in other programming languages directly from within SAS. We will show how GATEWAY can be used to integrate R and Python code in your SAS Programs, allowing you to take advantage of new open-source procedures to solve more complex coding tasks. We'll also show you how to take advantage of Viya's cloud capabilities to submit open-source programs in a way to take advantage of multi-threading, allowing you to really leverage large data or complex analyses |
proc step, proc iml, cas action, sas viya | source | 7 |
| 13 | A Novel ADEFF Design with Varying Baseline Type Selections | Tingting Tian, Chao Su, Fan Wang, Pengfei Zhu |
This paper outlines a novel ADEFF design used to analyze different baseline selections for primary and secondary endpoints. The percent change from baseline in a given lab parameter, measured at different time points can contribute to both primary and key secondary endpoints. Since the observed value for the lab parameter can be collected through different computation or test methods, results using the same method at baseline and post baseline time point of interest will be selected when available. To compute the change score for the lab parameter at different post baseline time points of interest and to compare results using different computation methods as sensitivity analysis, the proposed approach was to first pair the same computation/test method between the baseline and the time point of interest, then select the appropriate computation/test method for post baseline time of interest time points | secondary endpoint, percent change from baseline, change from baseline, primary endpoint | source | 6 |
| 14 | A Quick Start Guide to Writing a Technical or Research Paper | Kirk Paul Lafler | Writing a technical or research paper is an essential skill needed by academics, researchers, scientists, and many professionals as it permits the effective communication of literature research, methodologies, and findings to others in your profession and to the world. So, how do you begin writing a technical or research paper? This quick start guide will provide step-by-step instructions, along with a few valuable tips and examples, to help you effectively navigate the writing of a technical or research paper. Regardless of whether you're already a skilled writer or just starting out, you will learn the essential elements and structure that is essential to effectively communicate your research and findings to others in a technical or research paper | data science, exploratory data analysis | source | 10 |
| 15 | A SAS Macro Calculating Confidence Intervals of the Difference in Binomial Proportions from Stratified Analysis using the Miettinen & Nurminen Method with Cochran-Mantel-Haenszel Weights | Mikhail Melikov, Brian Mosier |
The Miettinen & Nurminen (MN) method with the Cochran-Mantel-Haenszel (CMH) weighting strategy is often recommended for constructing confidence intervals for the common risk difference in stratified analysis. However, it has not been implemented into SAS 9.4/SAS Studio for stratified analysis, although it is available in SAS Viya. We acknowledge that many Biostatisticians and Statistical programmers in the industry are working with SAS 9.4/SAS Studio, therefore we implemented the MN method with CMH weights (Lu, 2008) in a SAS Macro to make this analysis available for a broad SAS user audience. The SAS macro calculates the common risk difference in stratified analysis, confidence intervals at user defined significance levels, and the corresponding p-value. We do not expect the SAS macro to be dependent on operating system or SAS software version | macro definition, confidence interval, create table, sas studio, significance level, proc sql, proc means | source | 9 |
| 16 | A SAS System 7-zip macro that creates a zip archive and a file archive macro: versioning in the context of a private library of programs. | Kevin R. Viel | Versioning of files is essential in any project, but especially in a regulated industry, when the project has multiple resources (programmers, project managers, biostatisticians, et cetera), and in a fast -paced environment. When a programmer obtains a program from another project/delivery or in the midst of a complicated update with changing specifications, like saving a file while working on it, keeping a version of an updating file, a snapshot, is wise and helpful. While mature, well-developed versioning platforms are freely available, they may not be approved for use and have a learning curve. The goals of this paper is to describe a SAS System macro that uses 7-zip, a file archiver with a high compression ratio (https://www.7-zip.org/), a macro based on it to create a zip archive and its corresponding table of contents file (CSV), and macro to demonstrate the creation of an archive |
macro parameter, macro function, proc step, macro quoting function, filename statement, proc datasets | source | 26 |
| 17 | A SAS System PowerShell macro to report directory-level metrics by Owner (programmer). | Kevin R. Viel | In the Windows operating system, the Owner of a file is typically the person who last saved a file, which we expect to be the author or responsible person for the file. The SAS System includes the FINFO() function, which returns file metadata, but not the Owner on the Windows operating system. The Windows PowerShell, however, does return the Owner. While auditing a directory or set of directories, such information is required by a Lead Programmer or other manager and can be used to provide metrics, such as the distribution of times required to execute programs or the use of programming techniques or code, such as macros. The goal of this paper is to describe a SAS macro, MAC_U_FINFO, to abstract file metadata such as DATETIME (of last modification) or bytes, and, a SAS macro, MAC_PS_GETCHILDITEM, which, if when on the Windows operating system, abstracts the Owner of a file |
macro definition, proc sql, proc step | source | 14 |
| 18 | A Step-by-Step Guide to Calculating Relative Dose Intensity in Solid Tumor Studies | Yan Xu, Pingping Xia, Jagadesh Mudapaka |
The Relative Dose Intensity (RDI) is an essential parameter used to measure the actual dose over a specific period relative to the planned dose. Calculating RDI in oncology solid - tumor studies is critical for evaluating treatment efficacy and patient outcome. This paper demonstrates a practical application of the RDI formula. Through a solid tumor study as an example, this paper illustrates how to derive each component in the RDI formula and accurately compute RDI, even in complex study conditions. It also talks about resetting the baseline weight for significant weight changes, while keeping the units of the numerator and denominator in the RDI formula consistent with the weight variable. The objective of this paper is to provide a detailed, step-by-step guide for calculating RDI in a solid tumor study, including discussions on scenarios with varying cycle lengths, incomplete cycles, and different units for the actual and planned doses | dose intensity, dose | source | 6 |
| 19 | Accelerating Data Discovery and Governance: Unlocking Insights with Metadata Management, Data Catalogs, and LLM Integration for Streamlined Regulatory Approval in Clinical Trials | Pritesh Desai, Samiul Haque |
Data is the fuel that drives analytical decision engines. Both raw and curated data must first be discovered, prepared, managed, and made accessible to appropriate users in an analysis ready format. Effective metadata management and comprehensive data catalogs form the foundational pillars of agile data governance. Accessible and transparent data catalogs will be the cornerstone of digital transformation strategies. The modern SCE platform, significantly accelerates metadata management and analysis, drastically reducing the time and effort analysts spend on data discovery and preparation. Data catalogs aim at managing metadata for delivering a concrete classification of data across your organization so it can be easily accessed and consumed by businesspeople (self-service). Information about the localization of data is key. Examples include, where it comes from (lineage), how datasets can be used, and if they are recommended or certified by other users | sas viya, metadata management, data dictionary, rest api, data science | source | 7 |
| 20 | Achieving Reliable Data Verification with R: Proven Tools, Best Practices, and Innovative Workflows | Valeria Duran, Xuehan (Emily) Zhang |
In the pharmaceutical industry, ensuring the accuracy and reliability of data is critical, particularly when the output data can influence the analysis result. For SAS programmers transitioning to R or current R programmers exploring the language's capabilities in verification, identifying specific tools and best practices can be challenging. This paper presents the tools we can use for effective verification. We will focus on three levels of data verification: complete independent verification (double pro gramming), targeted independent verification, and peer review. We discuss how these levels align with varying risk levels associated with data complexity and the criticality of study endpoints. We will highlight our verification practices in R, showcasing how R functions from packages such as {testthat} and {purrr} can act as alternatives to SAS's PROC COMPARE |
data review, independent qc, proc compare, data wrangling, statistical programmer, proc step, pharmaceutical industry, data cleaning | source | 15 |
| 21 | Active Social Engagement in Remote Working Environments | Christine Reiff | Many companies have moved to a Work From Home model, where employees spend little or no time in an office with their peers. This model works well for hiring and retaining talented staff, especially as fewer people are willing to relocate, but can lead to feelings of disconnection and isolation. Gone are the days when we would meet in the break room to catch up with our coworkers over a cup of coffee or enjoy the donuts someone brought to celebrate Friday. These interactions allowed us to see our coworkers as more than just resources, but also as human beings with joys and struggles of their own. Today we use tools like email, Teams, or Slack to communicate, but conversations tend to focus on work, and our coworkers turn into just a pair of hands behind a keyboard. Engaging employees on a social level builds team bonds and personal connections. It helps us to understand each other, and from that understanding comes increased compassion, kindness and collaboration | source | 11 | |
| 22 | ADaM and TFLs for Drug-induced Liver Injury (DILI) Analysis | Song Liu, Rambabu Sura |
In August 2022, the FDA CDER released guidance for standard safety tables. Drug-induced liver injury (DILI) is part of the analysis supporting new drug application. Sponsors often use Hy's Law criteria to create table and figures for this analysis. The paper will explain Hy's Law criteria and present how to set up ADaM data from SDTM.LB. The maximum value of the post-baseline result should not only create Ratio to Analysis Range Upper Limit flag for subjects with the baseline results in normal ranges, but also Ratio to Baseline Value flag for subjects with baseline above the normal range. Sponsor may use the first ratio to do the analysis or use both when there are subjects enrolled study with abnormal results in ALT/AST and total bilirubin. ADaM data specification with blind dummy data illustration will be presented to show the Hy's Law can identify potential DILI cases | adverse events, safety population, cdisc | source | 7 |
| 23 | ADaM Fundamental Principles vs. Rules: Which to Follow, When, and Why? | Sandra Minjoe, Mario Widel |
The two of us authors have been actively involved in the CDISC ADaM team a long time - one for more than 20 years, and the other for over 15 years. During that time, we have led and contributed to a variety of ADaM documents, and we are both authorized CDISC ADaM trainers. In our years of working with ADaM, we've seen many different interpretations of ADaM documents - some interpretations are correct, others are not. This paper and presentation aim to address the confusion we have seen on ADaM Fundamental Principles vs. Rules, describe when each are needed, propose a hierarchy, and recommend how to handle compliance. We provide examples, focusing on tricky cases where there can be multiple correct options for implementation | adam dataset, raw data, cdisc | source | 6 |
| 24 | ADaM implementation for Anti-drug Antibody Data | Jiannan Kang, Luke Reinbolt |
ADA is the term which refers to Anti-drug antibodies generated by administration of Therapeutic Proteins (TP) as immunogenicity response. ADA can significantly influence drug exposure, bioavailability, safety and efficacy. ADA analysis is becoming more prevalent in drug development. The CDISC ADaM team has created a sub team to investigate the implementation and standardization of ADA analysis dataset. This poster is to share the proposal of ADA analysis dataset ADaM implementation. It covers from overview of multi-tier immunogenicity assessments, key data points included in ADA analysis, to a new structure to be proposed with highlights about dataset metadata, analysis parameters, variable metadata, data examples and summary of reporting/analysis | sample, adam dataset, elapsed time, proc contents, flag variable, follow-up period, cdisc | source | 19 |
| 25 | ADaM Pet Peeves: Things Programmers Do That Make Us Crazy | Sandra Minjoe, Nancy Brucken |
Both authors have been actively involved in the Clinical Data Interchange Standards Consortium (CDISC) Analysis Data Model (ADaM) team for a long time - one for more than 20 years, and the other for over 10 years. During that time, we've led and contributed to a variety of ADaM documents, and we are both authorized CDISC ADaM trainers. Because of our extensive ADaM expertise, we each end up reviewing a lot of ADaM submissions before they are sent to regulatory agencies. This paper highlights some of the common issues that we've seen ADaM developers make. For each issue, we provide a better and/or more conformant approach | adam dataset, controlled terminology, sdtm domain, overall survival, cdisc controlled terminology, cdisc | source | 13 |
| 26 | Addressing Challenges in Real-World Evidence Generation: The AI-SAS for Real-World Evidence Approach | Takuji Komeda, Yuki Yoshida, Yohei Komatsu, Yoshitake Kitanishi |
Shionogi has developed a product, AI-SAS, that semi-automates programming tasks for clinical trials, leading to a 33% reduction in working hours. Our achievement, including AI-SAS, earned first place in the Innovative Problem Solver category at the 2024 SAS Customer Recognition Awards. Additionally, Shionogi is offering AI-SAS externally as part of our social contribution initiatives. In July 2024, the FDA issued guideline enabling the use of Real-world Evidence (RWE) in drug approval applications. Shionogi is expanding AI-SAS to generate RWE with plans to integrate generative AI technology. We develop a system using SAS Viya, which facilitates the implementation of machine learning and deep learning. To improve transparency, we identified key requirements: predefining analysis content in the protocol and statistical analysis plan (SAP) before conducting the analysis, creating analysis result reports, and ensuring consistency between document creation and analysis timings | clinical trial, data science, machine learning, sas viya | source | 7 |
| 27 | Addressing early challenges in RWD data standardization for analysis and reporting of RWE studies | Xingshu Zhu, Li Ma, Bo Zheng |
The increasing reliance on Real-World data (RWD) in medical research necessitates efficient methods for data transformation to utilize modular standardized codes. The fragmented non -standard format of RWD makes it inefficient and complicated to generate reproducible Real-World Evidence (RWE). This paper explores a conversion process of RWD into a Basic Data Structure (BDS) format similar to Analysis Data Model (ADaM). It aims to explore a methodology of identifying common data elements in RWE data and mapping them to an ADaM-like BDS specifications format to facilitate use of existing standard code for RWE analysis. These efforts lay the groundwork for reproducible generation and analysis of RWE | adam dataset, clinical trial, bds, clinical data, sample, best response, laboratory data, data science, raw data, cdisc | source | 9 |
| 28 | Advanced ODS Excel Tricks: Password Protected Workbooks and Clickable Navigation | Jeffrey P. Meyers | The Microsoft EXCEL destination was added to the SAS output delivery system (ODS) in version 9.4M3 and offered an improvement over the EXCELXP tag sets destination. The EXCEL destination allows SAS users to create workbooks in the XLSX format instead of the XML format which reduces the file size and unlocks additional functionality. This paper focuses on two advanced tricks when using the EXCEL destination: password protection and clickable navigation. The EXCEL destination can create a "protected" workbook but does not include the option to include a password which allows any user to simply remove the protection from within EXCEL. This paper will show techniques to add password protection to the EXCEL file and how to selectively choose which worksheets or columns are protected. The second focus of this paper, clickable navigation, allows the user to click on a cell within a worksheet to jump to another cell anywhere in the workbook leading to very fluid and dynamic reports |
excel, proc report, xls, character variable | source | 13 |
| 29 | Advanced Programming with R: Leveraging Tidyverse and Admiral for ADaM Dataset Creation with a Comparison to SAS | Joshua J. Cook, Richann J. Watson |
The transition from SDTM to ADaM data sets is a critical step in clinical trial data preparation, requiring precise programming solutions to ensure compliance with CDISC standards. This paper presents a two- stage approach to ADSL derivation: first, we manually derive key variables using base R and the tidyverse, demonstrating the detailed logic required for compliance and flexibility in handling real-world data challenges. Then, we explore the admiral package, showcasing how its specialized functions significantly simplify and streamline this process. By leveraging admiral, we automate derivations such as treatment start and end dates (TRTSDT, TRTEDT) and treatment duration (TRTDUR) while addressing duplicates, validating data set integrity, and enhancing reproducibility. We highlight the power of admiral in minimizing code redundancy, integrating custom logic, and ensuring seamless compliance with industry standards | clinical data, clinical trial, if-then-else, reference data, proc step, proc transpose, feature engineering, adverse events, data wrangling, vital signs, cdisc | source | 20 |
| 30 | AI Empowered Metadata Governance | Prasoon Sangwan | Efficient metadata governance is pivotal for seamless digital data flow, ensuring streamlined data collection, analysis, and standardized transformations. It cultivates standardization, harmonization, reusability and automation. Despite the evolving importance of metadata, complex trial designs often complicate governance making it an intricate process. This leading to siloed standards protocol specific nuances further exacerbate the challenges, justifying deviations from established norms. This paper explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in enriching metadata management. AI and ML present a paradigm shift through metadata discovery creating sponsor aligned metadata standards, facilitating rapid transitions between multiple versions of standards, and maintaining the lineage between standards | clinical trial, artificial intelligence, data cleaning, metadata management, data science | source | 9 |
| 31 | AI is coming for you: New Biometric Leadership in the era of Gen AI | Kevin Lee | In the contemporary landscape of technological advancements, the integration of Gen AI like ChatGPT has ushered in a new era of innovation and development. This paper will discuss how these cutting-edge technologies influence biometrics department, then how biometrics leadership will lead this innovation. The paper will start with the introduction with Gen AI, then the use case of ChatGPT in Biometric tasks such as coding, data analysis and exploration, contents development, and more. It will also provide how ChatGPT has contributed to a remarkable 40% increase in productivity among skilled workers, indicating the transformative power of ChatGPT. To harness this transformative potential, biometrics leadership must evolve beyond traditional management paradigms. The paper will explore how effective leaders are reimagining their roles as AI integration architects, focusing on three critical dimensions: system, process and people | clinical research, data cleaning, clinical trial, phase iii, data integrity, adverse events, data science, secondary endpoint | source | 7 |
| 32 | AI Performing Statistical Analysis: A Major Breakthrough in Clinical Trial Data Analysis | Toshio Kimura, Siqi Wang, Weiming Du, Songgu Xie |
Statisticians and statistical programmers in the pharmaceutical industry are dreaming about AI performing analysis, but this has not yet been demonstrated. Companies are only experimenting with AI generated code. AI is not integrated into system workflows or connected to analysis infrastructure … until now. This paper will demonstrate a proof of concept (PoC) showing AI performing statistical analysis. The chat interface will be used to initiate, parameterize, and execute statistical analysis. To achieve this, we mapped out a detailed workflow of the current process including the dialog between the statistician and statistical programmer. After which, we then developed an AI solution to operationalize this workflow. The solution uses Python as the backbone to seamlessly integrate ChatGPT and runs SAS as the analysis engine | data science, statistical programmer, pharmaceutical industry, clinical trial, clinical data, macro definition, artificial intelligence | source | 11 |
| 33 | AI Search LOG | Zhuo Chen, Martha Cao, Ted Lystig, Sateesh Arjula |
In a clinical study, it is important to make sure LOG files generated by SAS programs are clean. Search LOG files maybe needed daily during the study program development cycle for SDTM, ADaM, Tables Listings and Figures etc. This paper describes an automated method to reduce human labor in searching LOG efforts therefore helps team to gain efficiency | sas batch, proc freq, sample, cdisc | source | 8 |
| 34 | AI-Assisted Transition: from SAS to RStudio for PK Summary Analysis | Shiqi Lin | SAS has traditionally been the primary tool for data analysis and reporting in the pharmaceutical industry. However, the increasing popularity of R and its open-source nature have created a growing demand for program conversion. Manual conversion is time-consuming, error-prone, and requires significant expertise. This paper discusses a Gen AI-assisted approach aimed at streamlining the conversion from SAS to R, enabling SAS programmers with limited experience in R to initiate the conversion process. Using a pharmacokinetic (PK) summary table program as a case study, we decompose a SAS macro into key modules, convert it to R code with the assistance of a Gen AI tool, and compare the results between SAS and R to assess the accuracy of this approach | macro definition, sample, geomean, pk parameter, confidence interval, dataset, if-then-else, in=, sas syntax, proc mixed, proc means | source | 9 |
| 35 | AI-Powered Data Issue Tracker for Efficient Data Issue Tracking and Resolution | Bharath Donthi | Traditional methods for documenting and resolving clinical data issues, often reliant on spreadsheets shared among statistical programmers, statisticians, clinical data managers, and sponsors, present significant inefficiencies. These methods can lead to redundant reporting of similar issues, difficulty in tracking resolution status, inadequate detection of duplicates, and a lack of comprehensive audit trails. To address these limitations, a system incorporating Artificial Intelligence (AI) has been developed. This system facilitates structured issue entry, assignment, real-time status tracking, and maintains a robust audit history. Key AI components include semantic analysis for duplicate issue detection, natural language processing for user interaction, and automated generation of SQL queries for data investigation | proc sql, audit trail, clinical data management, data cleaning, clinical data, clinical research, data governance, duplicate detection, adverse events | source | 6 |
| 36 | Amazing Graph Series: Advanced SAS Visualization to Wake Up Your Data - From Static to Fantastic | Tracy Sherman, Aakar Shah |
In an era where data drives decision-making, the ability to interact dynamically with visualizations is crucial for uncovering insights and streamlining workflows. SAS 9.4 provides advanced functionality within the SGPLOT procedure to create interactive visualizations with features such as hover-over data points, drill-down capabilities, and zoom out options using ODS HTML with imagemap capabilities. These advanced techniques empower users to explore data more effectively, facilitating routine data reviews with increased efficiency and precision. This paper demonstrates the step-by-step creation of these interactive visualizations, emphasizing their practical application and visual aesthetics. By exploring the capabilities of the SGPLOT procedure, this paper aims to equip users with the tools to transform static graphs into engaging, actionable visualizations suitable for routine data reviews |
ods html, proc sgplot, macro variable, proc step, data review | source | 10 |
| 37 | An End-to-End Workflow for TFL Generation using R: MMRM Applications and Comparative Insights with SAS | Kai Lei, Jiaqiang Zhu, Margaret Huang |
Tables, Figures, and Listings (TFLs) are essential for presenting clinical trial data in regulatory submissions, ensuring transparency and compliance, and they are traditionally generated using SAS. With the rise of R as an open-source, flexible, and cost-effective alternative, this paper presents an end- to-end workflow for TFL creation in R, using Mixed-Effects Models for Repeated Measures (MMRM) as a representative application example. Our workflow covers data preparation, model implementation, TFL generation, and regulatory-compliant output formatting, leveraging R packages like dplyr, ggplot2 and r2rtf. A side-by-side comparison of R's mmrm package with SAS's PROC MIXED highlights the differences in syntax, flexibility, and visualization capabilities, demonstrating R's advanced options for custom graphics and layout adjustments |
proc mixed, clinical trial, regulatory submission, proc step, covariance, change from baseline, degrees of freedom, estimated marginal mean, covariance structure, ods rtf, data cleaning, compound symmetry, treatment effect, data wrangling, pharmaceutical industry, raw data, clinical data, correlation, missing value, data science | source | 14 |
| 38 | An Integrated R Shiny Solution for Dynamic Subgroup Adjustments and Customizations | Yi Guo | A major challenge in using SAS macros and traditional R tools such as ggplot2 for figure generation arises when categories within a variable change with dataset updates. Such changes, including the addition of new categories as data accumulate or the exclusion of existing ones due to u pdated filtering criteria, require code adjustments to subgroup settings like ordering, colors, markers, or other style options. Done manually, this process is inefficient, error-prone, and particularly challenging in clinical trial reporting, where data get frequent updates and accurate and timely visualizations are critical. While effective for static visualizations, these tools lack flexibility for dynamic, no-code customization, limiting their scalability. To address this, we introduce an integrated set of interactive features powered by R Shiny, including automatic subgroup detection, interactive legend reordering, and customizable color pickers | class variable, swimmer plot, macro definition, clinical trial, kaplan-meier plot | source | 20 |
| 39 | An Introduction to Obtaining Test Statistics and P-Values from SAS and R for Clinical Reporting | Brian K. Varney | Getting values of test statistics and p-values out of SAS and R is quite easy in each of the software packages but also quite different from each other. This paper intends to compare the SAS and R methods for obtaining these values from tests involving Chi-Square and Analysis of Variance such that they can be leveraged in tables, listings, and figures. This paper will include but not be limited to the following topics: * SAS ODS TRACE |
output, anova, dataset, proc step, proc freq, output delivery system, ods output | source | 9 |
| 40 | An Introduction to the Role of Statistical Programming in Medical Affairs | Nagadip Rao | Medical Affairs (MA) is a specialized division within research-based pharmaceutical companies, dedicated to scientific communication, education, and ensuring the safe and effective use of products. MA plays a pivotal role in supporting the commercialization process and overseeing post-market activities. The MA team comprises interdisciplinary professionals, with statisticians and statistical programming teams being integral to its operations. This paper provides an overview of MA's functionality within the industry, highlighting the role of statistical programming. Additionally, it discusses the nature of deliverables, challenges faced, and presents a real-life example of how statistical programmers support MA in achieving its objectives | clinical programming, statistical programmer, clinical trial, data visualization, raw data, sample, clinical data, data science | source | 9 |
| 41 | An Introduction to Using PROC S3 in SAS to Access and Manage Objects in Amazon S3 | Kevin Russell, Russ Tyndall |
Amazon S3 (AWS S3) is an object storage service that enables customers to store, manage, analyze, and protect their data. Millions of customers worldwide now use the scalabilit y, data availability, improved security, and performance of AWS S3 to store their data. Many of the companies now taking advantage of AWS S3 storage are also SAS customers, so SAS has provided its customers with multiple ways to interface with AWS S3. One of the primary methods SAS provides to interface with AWS S3 is the S3 procedure. PROC S3 is a Base SAS procedure that is available in both SAS 9.4 and SAS Viya, which enables SAS customers to perform object management in AWS S3. PROC S3 enables customers to create buckets, directories, and files in AWS S3. PROC S3 also enables customers to list, copy, and delete objects. The purpose of this paper is to do as follows: * Provide an overview of PROC S3 and the basic steps needed to start using the procedure |
proc step, amazon s3 | source | 10 |
| 42 | Application of advanced GenAI tools in sample size estimation - questions and thoughts | Igor Goldfarb, Ella Zelichonok |
The last couple of years have been characterized by storming expansion of Generative Artificial Intelligence (GenAI) and leading pharma companies plan to invest billions of dollars in development of GenAI in upcoming years. The goal of this work is to discuss prospective benefits and to increase awareness of investigators, scientists and statisticians about the application of GenAI to calculate sample size for prospective clinical trials. The authors assessed the performance, benefits and risks of using the latest versions of popular GenAI tools for sample size calculations in clinical study design. ChatGPT, Copilot and other tools were chosen as the popular GenAI products. The publicly available (ClinicalTrials.gov) results (sample size used in the completed study) were replicated using GenAI tools. The authors found that the advanced versions of GenAI tools perform much better than the previous ones, while still demonstrating some errors | sample size, sample, artificial intelligence, chi-square test, clinical trial, exact test, phase ii, fisher exact test, proc power and sample size, life sciences | source | 12 |
| 43 | Approaches to Developing Multiple Imputation ADaM Datasets | Kang Xie | Multiple Imputation (MI) is a statistical technique used to handle missing data by generating multiple complete datasets. These datasets are created by imputing missing values based on observed data, and each dataset is analyzed separately. The results from these analyses are then combined to provide a final inference. This paper describes three examples of multiple imputation ADaM datasets generated by incorporating the imputed values into the ADaM dataset structure, maintaining the integrity of the analysis-ready format. Each example corresponds to a distinct multiple imputation method. The MI ADaM datasets include: 1) variables used in the imputation process and variables to identify the multiple imputation method, ensuring clear traceability, and 2) concatenated imputed values derived from the multiple imputation process. Furthermore, this paper introduces the use of the PARQTYP and PARQUAL variables in the multiple imputation ADaM datasets | imputation, multiple imputation, adam dataset, proc mi, proc step, missing value, cdisc | source | 8 |
| 44 | Association of Alzheimer-s Disease with Cardiovascular Disease and Depression in Older Adults: Findings from the 2018 Nationwide Inpatient Sample | Prayag Shah | Background: Alzheimer's Disease (AD) and Alzheimer's Disease -Related Dementias (ADRD) significantly impact 6.9 million American older adults, particularly in those with major cardiovascular diseases (CVD) such as Stroke, Congestive Heart Failure (CHF), and Coronary He art Disease (CHD), along with comorbidities of hypertension (HTN) and depression. Yet their influence is not fully understood, particularly in hospitalized populations. This study investigates how major CVD forms and comorbidities affect the odds of AD/ADRDs and examines their impact on risk of in -hospital mortality among hospitalized older adults. Methods: We analysed data from the Nationwide Inpatient Sample (NIS), a nationally representative database. Amongst the older adults, 148,772 admissions with a primary AD/ADRD diagnosis were identified. Of them, 15709 died within 30-day hospitalization | sample, binary variable, regression analysis, hazard ratio, mortality, logistic regression, data science, odds ratio, proc phreg, logistic distribution, likelihood function | source | 27 |
| 45 | Automated Word Report Generation for Standardized CMC PC Study with Programmatically Inserted Contents in R | Song Liu, Jiannan Kang |
Biologics process characterization (BPC) study reports have prescriptive formats and contents. An internally developed BPC-Stat tool (JMP add-in) can produce Microsoft Word reports containing statistical analysis results. But this output does not have the reference linking and required outline structure. Manual efforts and additional contents such as description of the model effects are still required from the project scientists. This paper demonstrates a new tool to use R/R Markdown/R shiny to automatically generate the Word report file with standard format, consistent language, auto- populated interpretation texts, and auto- generated TOC, TOT, TOF, header/footer, content -driven cross-references, and prompts. With this tool, PC Study Report is automatically generated with a few user inputs of document number, document title, etc. and one click of the download button from the R shiny app which shared by the developer | data science, work efficiency | source | 16 |
| 46 | Automating Define-XML Updates: A SAS-Based Framework for Submission Readiness | Qiong Wei, Lixin Gao |
The data definition file (define-xml), which describes the metadata of the submitted clinical data, is one of the most critical components of electronic data submission for regulatory review. Therefore, it is essential to ensure that the Define-XML file is consistently aligned with the latest datasets and complies with the most current standards. In practice, however, various factors can lead to changes in datasets after the Define-XML has been created. Under these circumstances, updating an existing Define-XML becomes a crucial but complex and time-consuming task in preparing metadata for regulatory submissions. This paper introduces a SAS-based framework designed to automate the Define-XML update process, providing an efficient and reliable solution for metadata management. The framework utilizes SAS macros to extract metadata from the existing Define-XML, integrate it with updated dataset specifications, and generate a revised Define-XML that adheres to the latest standards | macro definition, dataset, protocol amendment, excel, data cleaning, adam dataset, cdisc | source | 10 |
| 47 | Automating Recurring Data Reconciliation for Serious Adverse Events Using SAS | Patrick Dowe, Gina Hird |
Sometimes while working on a project, data reconciliation is required. This task may present itself as a regular, recurring task or a one-time need. In our use case, we will be showing a program built specifically for recurring data reconciliation of serious adverse events; however, the applications for this SAS program are much broader and could be applied to a host of situations. The saereconciliation.sas program has two major components: First, reconciling the SAE vendor's Pharmacovigilance safety database, in EXCEL format in this example, with a SAS dataset from the clinical database containing the Adverse Event data. The program does a series of cascading matches by key variables until all records have been accounted for. After the matching is complete, the differences between data points are highlighted for the clinical safety team so that they can follow up with the SAE vendor and the sites |
adverse events, serious adverse event, excel, dataset, clinical trial, composite key, foreign key, macro variable | source | 25 |
| 48 | Automating SAS Program Header Updates with Macros | Kexin Guan | Maintaining accurate and comprehensive documentation in pharmaceutical programming is essential for audit trails and program traceability. As clinical programming projects increase in complexity, manually updating program headers becomes a challenging and tedious task. Programmers often face difficulties in tracking multiple input datasets, output files, and macro calls across various SAS programs. This paper presents a solution as a macro designed to automate the generation and updating of program headers. The macro retrieves essential metadata such as input datasets, macro calls, program outputs, logs, and program flows from existing SAS programs, and seamlessly integrates them into the program header. The macro can process individual files or entire directories, providing flexibility across diverse programming environments | macro call, in= | source | 9 |
| 49 | Automating Superscript Display for Upper and Lower Limit of Quantification Values in Pharmacodynamic Tables | Anu Eldho | Pharmacodynamics plays a critical role in clinical trial oncology, as it helps to understand and evaluate the therapeutic effects and mechanisms of action of anticancer drugs. This paper aims to develop a macro that automates an essential and time-consuming part of superscripting the upper limit of quantification (ULOQ) and lower limit of quantification (LLOQ) values in the cytokine table outputs. This tool simplifies the task of biostatisticians, pharmacodynamic scientists, and medical writers in superscripting the outputs manually, which is time-consuming and prone to human errors. Scientists can accurately contextualize drug safety and efficacy conclusions by clearly labeling values below or above the validated range. ULOQ, the highest concentration of cytokine values, and LLOQ, the lowest concentration of cytokine values, define the range within which the analytical method can reliably and accurately quantify the concentration of a particular cytokine in a sample | lookup, clinical trial, sample, proc means, data cleaning, adam dataset, macro parameter, quality control, adverse events, cdisc | source | 7 |
| 50 | Automation of Trial Design Domains generation using AI | Prasoon Sangwan, Mayank Bhatia, Charusheela Thakur, Shailesh Kumar |
Trial Design Domains are an integral part of Study Design Tabular Model designed with the objective of holding study design information. These datasets assist in creation of a trial design knowledge base providing a quick glimpse of its key essentials design features like comparison of treatments, subject criteria, trial paths etc. Processing Trial Design Domains becomes little cumbersome since majority of the information is captured from the protocol which is an unstructured document instead of the data collected for the subjects. This paper will capture how Artificial Intelligence and Machine Learning can help generate the datasets extracting data from the protocol, CRF , and a set of other SDTM domains. It will explain how a group of models can be ensembled together to classify diƯerent sections of the protocol and extract the specific elements for each trial design domain | clinical trial, protocol amendment, artificial intelligence, tolerance interval, cdisc | source | 8 |
| 51 | Avoiding the Ouch: Mastering Time to Pain Progression Analysis | Kavitha Boinapally, Sai Krishna Pavan Nakirikanti, Hank Dennis, Yang Wang |
In Oncology studies, time to pain progression (TTPP) is an endpoint that measures the time from randomization or treatment start to a patient experience a significant worsen ing of their pain. While Progression Free Survival (PFS) and Overall Survival (OS) are the typical primary endpoints, TTPP can be selected as a secondary endpoint to assess disease progression and treatment efficacy. In this study, the worsening of pain is defined by an increase of 2 or more points on Brief Pain Inventory Short Form (BPI - SF) Question 3 (pain at its worst in the last 24 hours) or an initiation of new Opioid recorded in BPI -SF Question 7 (what treatment/medications are you receiving for pain) for at least two consecutive assessments. In this paper, we report how we use patient reported outcome (PRO) from the BPI- SF information to assess TTPP endpoint. We describe how BPI -SF data is collected, mapped to SDTM and ADaM domains, and used to derive and analyze pain worsening | randomization, clinical endpoint, clinical trial, cdisc | source | 9 |
| 52 | BDS Dataset with PARQUAL and PARQTYP Variables for Time-to-Safety Events Analysis | Kang Xie | An analysis of adverse events can use the ADaM datasets, specifically ADAE or ADTTE. ADAE is OCCDS data structure, and ADTTE, structured in the BDS with additional time-to-event (TTE) variables, specifically supports survival analysis related to censoring factors, making CNSR a required variable. This paper describes an approach to creating a BDS Dataset named ADSTOI (a name given by the author), which is designed for the analysis of time to various events related to safety topics of interest (STOI) by grade level and treatment cycle within the oncology therapeutic area across four scenarios that do not involve censoring factors. The ADSTOI design aims to provide clarity and traceability in the calculation process of time to various events | data science, adverse events, cdisc | source | 9 |
| 53 | Benefits, Challenges, and Opportunities with Open Source Technologies in the 21st Century | Kirk Paul Lafler, Ryan Paul Lafler, Joshua J. Cook, Stephen B. Sloan, Anna T. K. Wade |
Organizations around the globe are truly facing a paradigm shift with the type of software, the quantity and availability of software technologies, including open source, and the creative ways the many technologies live, play, and thrive in the same sand box together. We'll explore the many benefits, challenges, and opportunities with open source technologies in the 21st century. We'll also describe the challenges facing user communities as they find ways to integrate open source software and technologies, handle compatibility and vulnerability issues, address security limitations, manage intellectual property and warranty issues, and address inconsistent development practices | data integration, data science | source | 7 |
| 54 | Better, Faster, Stronger: A Case Study in Updating SAS Macro Code with Run-time Optimization in Mind | Valerie Finnemeyer | We typically write code for a specific purpose, then "macrotize" it for more general use. Over time, additional features are tacked onto the original code, turning it into "Frankencode" that functions, but not always optimally. Ideally, we could start over when our original macro code becomes unwieldy, building a new version of it from the ground up. But this may not be practical, and the resulting improvements may not justify the time we'd have to spend rewriting the code. This paper describes a process for updating complex macros to reduce runtime. It then illustrates how to use this framework by applying it to a CBAR macro that performs and aggregates descriptive statistics from a CDISC ADaM BDS or ADSL dataset to create publication-ready tables. I describe the three-step process: brainstorming potential improvements, testing and assessing their impact, and * deciding how to update the macro to minimize programmer effort and maximize user eff iciency gain | proc step, macro definition, proc freq, proc sort, by group processing, cdisc | source | 10 |
| 55 | Beyond Basic SG Procedures: Enhancing Visualizations in SAS Graphic | Vicky Yuan, Fengying Miao |
SAS statistical graphics (SG) procedures provide powerful tools for generating high-quality graphs. However, users often encounter limitations when customizing plots beyond the standard options. This paper introduces SG annotations, SG discrete attribute maps, AXIS tables and SGPANEL, advanced techniques that extends the capabilities of SG procedures by allowing users to precisely control graphical elements, add custom labels, lines, shapes, and other enhancements. Through practical examples, we demonstrate how these techniques can be applied to various plot types, demonstrating techniques to refine presentation graphics and tailor statistical displays to specific analytical needs. By leveraging SG annotations, SG discrete attribute maps, AXIS tables and SGPANEL, users can create a graphic with more information, visually compelling and highly customization. The SAS code used in this paper is using SAS@ 9.4 SAS Enterprise environment | proc step, proc sgplot, sg annotation, proc sgpanel | source | 10 |
| 56 | Beyond Tokenization: Considerations for Linking Healthcare Data Sets for Scientific Research | Jennifer Dusendang, Yuval Koren |
Linking data from disparate sources for scientific studies is highly valuable when using real-world data. A common example is linking electronic medical record (EMR) data to medical claims data or data from specialized providers outside the EMR system. Although creating privacy-preserving record linkage (PPRL) tokens is part of the linkage process, additional methods and considerations are necessary to produce a reliable and usable linked data set for scientific research. Linkage rates provide an upper bound of data set patient coverage and usability for analyses. However, these are typically basic calculations of how often PPRL tokens match between two data sets, without regard to availability of key data elements, study period overlap, or issues with duplicate or low-specificity tokens. In particular, not identifying an appropriate timeframe in which linked data is applicable for a study can lead to unexpected decreases in sample size and limited study feasibility | sample size, sample, link analysis | source | 7 |
| 57 | Breaking Barriers in Clinical Trials: Insights into Platform Designs | Chetankumar Patel | Platform trials are changing how clinical research is done by allowing multiple treatments to be tested at the same time within one trial, specially in Oncology. These trials use a shared structure called a master protocol, which makes them flexible and efficient. A single control group can be used for multiple treatments, saving time and resources. One of the defining features of platform trials is their adaptive design. New treatments can be added as the trial progresses, and ineffective treatments can be discontinued based on interim data, allowing researchers to rapidly focus on promising therapies. As medical research moves forward, platform trials are becoming a key tool for improving the way new treatments are developed and tested. I will share the challenges we encountered while working on Platform Trial, the strategies and steps we took to address and resolve them, and the success we achieved through these efforts and experiences | clinical research, randomization | source | 11 |
| 58 | Breaking Down Silos: Empowering Pharma Commercial Teams Through Integrated Data Insights | Hector Campos, Naquan Ishman, Mike Turner |
Effective data visualization is crucial for navigating the complexities of pharmaceutical and biotech operations. AI-driven analytics and integrated visualization transform large datasets into actionable insights, enhancing decision-making across commercial, customer engagement, and market access strategies. The Problem Isn't Complexity-It's Integration In pharmaceutical commercial operations, data fragmentation across prescriptions, market data, and Customer Relationship Management (CRM) systems limit visibility, making it difficult to execute cohesive, data-driven engagement strategies. The challenge isn't just data volume-it's fragmentation. Without a unified, real-time data strategy, commercial teams lack the visibility needed to execute effective engagement strategies. The Cost of Disconnected Insights Without integration, executive teams operate in an information void, where physician engagement is driven more by intuition than by insights | business intelligence, analytics platform | source | 8 |
| 59 | Bridging RStudio and LSAF: A Framework for Faster and Smarter Task Execution | Jake Adler, Ben Howell, Lindsey Barden |
Open-source technologies are transforming today's clinical trials. Flexible open-source editors like RStudio are the perfect place to build dashboards that accelerate exploratory programming work. RShiny dashboards built on REST APIs connect directly to clinical data repositories. With available data, users can quickly create multiple TLFs and view them in real time. Any number of input parameter fields allow control over which studies, deliveries, domains, and variables are used. This paper will discuss ho w to create the dashboard, show examples of specific use cases where the dashboard helps save time, and offer inspiration for future enhancements to make the dashboard a more comprehensive, efficient clinical programming tool | group by, rest api, bar chart, clinical data | source | 9 |
| 60 | Bridging the Gap: Leadership of Statistical Programmers in Clinical Trials | Allison Covucci, Xiaohan Zou |
In the complex environment of clinical trials, various functions collaborate to deliver more medications to more patients faster, effectively, and efficiently-from protocol development, clinical trial startup, data collection, and database lock, to submission to Health Authorities (HA), disseminating scientific information, and ensuring the accessibility of new therapies to the public. Each function often operates from its own perspective, without a comprehensive understanding of how and where the data they handle will be utilized to support various objectives through statistical analyses. This is where the role of statistical programmers becomes pivotal. As the final users of the data, they possess a unique vantage point that allows them to see the entire process from data collection to statistical analyses. This presentation highlights the critical leadership qualities that statistical programmers possess to effectively act as a bridge between various functions | statistical programmer, clinical trial, data science, data governance, adam dataset, data review, database lock, clinical data, cdisc | source | 6 |
| 61 | Build vs. Buy: Strategic Considerations for Implementing AI Solutions in Pharma and Biotech Companies | Rajesh Hagalwadi | The rapid expansion of the AI market, projected to reach $407 billion by 2027, presents both opportunities and complexities for pharma and biotech companies. A critical decision these enterprises face is whether to build custom AI solutions or buy pre -existing ones. This paper explores the str ategic considerations involved in the build vs. buy dilemma, emphasizing the impact on speed, cost, and effectiveness of AI implementation. It discusses the advantages of buying pre -built solutions, such as immediate value, compliance with regulatory demands, and vendor support. Conversely, it highlights the benefits of building custom solutions, including tailored functionality and competitive advantage in strategic applications like drug discovery. The paper also addresses the importance of data readiness , business objectives, and the need for a dedicated team and infrastructure for successful AI deployment | clinical trial, data cleaning, resource allocation, data governance | source | 15 |
| 62 | Building a Scalable Training Platform for R: Empowering Analytics Excellence in Corporate Initiatives | Michelle Page-Lopez, Martyn Walker |
Building a scalable training platform for R within a Contract Research Organization (CRO) addresses the growing demand for advanced analytical tools in clinical research. As the industry shifts toward open - source programming, equipping staff with the necessary R skills becomes critical. This paper highlights the creation of a flexible, accessible, and comprehensive training framework that fosters skill development while meeting organizational needs. The training platform leverages modern e-learning technologies, combining self-paced modules, interactive coding exercises, and real-world case studies. We showcase the {learnr} package in R, which enables the creation of hands-on training content by combining narrative text, R code, quizzes, and visualizations. This approach is ideal for creating tutorials that are customizable, scalable, and accessible through cloud-based infrastructure | clinical research, clinical trial, statistical programmer, sample | source | 7 |
| 63 | Building Extensible Python Classes for Analysis and Research : It-s Easier Than You Think! | Sundaresh Sankaran, Samiul Haque |
Data Analytics in the life sciences industry requires iteration and access to a wide range of methods and techniques for trusted, stable and robust results. The Python open-source ecosystem provides a rich array of such methods through a number of packages and modules that promote rapid and flexible experimentation. However, such variety also has its downsides such as package dependencies, strict version support and a clutter of similar packages. Data scientists and programmers require a common framework which packages many capabilities in a seamless manner. In this session, we provide a design and example of a Python class which encapsulates established packages and pipelines for data management and outcome predictions, making them available from a single instance. These methods cover a range of operations across the analytics life cycle and are extensible to include new methods and classes | machine learning, sas viya, data science | source | 8 |
| 64 | Building Resilient Teams | Patrick Grimes, Sajin Johnny |
In the ever-evolving pharmaceutical landscape, characterized by Volatility, Uncertainty, Complexity, and Ambiguity (VUCA), the ability to build and maintain resilient teams is crucial for success in clinical research. This paper explores strategies for leaders to cultivate team resilience, enabling organizations to navigate challenges and seize opportunities in an unpredictable environment. We begin by examining the unique VUCA factors affecting the pharmaceutical industry, from regulatory shifts to global health crises. The discussion then investigates the key characteristics of resilient teams, emphasizing adaptability, diverse skill sets, and strong communication. Central to our exploration are practical leadership strategies for fostering resilience. These include creating a culture of continuous learning and implementing agile methodologies | clinical programming, clinical trial, data science, clinical data, clinical research, pharmaceutical industry, data integrity | source | 15 |
| 65 | Building Robust R Workflows: Renv for Version Control and Environment Reproducibility | Junze Zhang, Joshua J. Cook |
Reproducibility and consistency are vital in pharmaceutical analytics, where analyses must meet rigorous industry standards. Managing R environments effectively ensures consistent and reproducible results across projects, teams, and systems. This paper focuses on the use of renv, a powerful R package designed to achieve version control and environment reproducibility. We will demonstrate how renv allows users to create isolated project environments, lock package versions, and restore dependencies seamlessly across systems and teams, ensuring uniformity in analytical outputs. Attendees will learn practical workflows for initializing and managing renv environments, sharing them among team members, and resolving compatibility challenges. The session will also explore how renv integrates with version control systems, such as git, enabling teams to manage R environment changes alongside code revisions, thus reducing errors and enhancing collaboration | adverse events, clinical trial, regulatory submission | source | 17 |
| 66 | Calculating exact posterior probabilities and credible intervals from Bayesian borrowing robust mixture priors for binary, count and continuous outcomes in R and SAS | Darren Scott, Armando Turchetta |
Recently, Bayesian statistical methods have been developed that leverage historical clinical trial information to assist in the discovery, development, and delivery of medicines. "Dynamic" models preserve the primacy of the data in the target study by discounting the external information according to how closely it matches the target data. A popular approach for "dynamic borrowing" is a robust mixture prior (RMP), where an informed element(s) is combined with a vague element and a prior weight. Typically, in the Bayesian paradigm inference is performed through various Monte Carlo (MC) sampling schemes. A prudent choice of RMP leads to a conjugate mixture posterior for the control and treatment parameter. In this article we provide insights into how simple mathematical concepts lead to the application of numerical integration in R and SAS to obtain fast an accurate inference from the posteriors after Bayesian borrowing | treatment effect, posterior probability, likelihood function, credible interval, clinical trial, proc iml, binomial distribution, proc step, monte carlo | source | 20 |
| 67 | Calculating the Physical Length of a PDF File String with 'Times New Roman' Font by SAS | William Wu, Steven Li |
When using the PROC REPORT procedure with ODS PDF statement for output table/listing generation, people often wish to know the 'Physical Length' of a string, instead of the number of characters in the string. The physical length of a string could be used to decide the column's minimum width without word wrapping up, or without overlapping to the next column. This paper created a custom SAS function 'pwidth()' through PROC FCMP to calculate the physical length of a string on font 'Times New Roman' in PDF file. It may be useful for generating a nice FDA's BIMO (Bioresearch Monitoring) PDF Listing |
ods pdf, proc report, proc step | source | 22 |
| 68 | Catch Page Overflow Issues Quick and Easy - A Simple Python Solution | Junze Zhang, Huei-Ling Chen |
Clinical study reports often contain many tables, listings, and figures. When content spills over the edges of a page, it can result in overflow issues. While these issues may be easily identified in smaller files by manually reviewing the document, scrolling through hundreds of pages to manually check the overflow is mission impossible. Therefore, it is essential to develop an efficient quality control tool to detect these overflow problems early in the process, reducing the chance of having to perform re -runs. Many pharmaceutical companies and contract research organizations utilize the Rich Text Format (RTF) file format for clinical study reports. This paper introduces a straightforward Python function designed to automate this task. The function efficiently checks RTF files in batches, offering rapid verification even when processing a large number of tables, listings, and figures (TLFs) | clinical trial, csr | source | 6 |
| 69 | Change the way you think of Codelist - Optimize managing Organization Controlled Terminology with a meta-model and its features to manage Codelist in MDR | Kairav Tarmaster | Each business function (Data Collection, SDTM, ADaM) in Data Standards team governs and releases the metadata for their respective data model very well. To realize the absolute benefits of a Controlled Terminology (CT), various business functions such as Data Collection, SDTM, ADaM, and ARS must collaborate to deliver an Organization CT. The aim should be to maximize the use of NCI Dictionary with extensions across studies, eliminate redundancy with unique codelist definitions used across data models, help standard and study teams assess the impact of changing a codelist, assist in collecting clean data with codelist subsets and extensions, establish conformant mappings between Data Collection and Submission for metadata-driven data transformation, identify extended code terms in submission deliverables (define.xml) in studies and have the flexibility to extend the CT as new models (USDM) gets implemented in your organization | cdisc controlled terminology, data transformation, raw data, data standard, proc freq, cdisc | source | 11 |
| 70 | Checking Outside the Box: A Framework for Submission Success | Julie Ann Hood, Seiko Yamazaki |
Preparing SDTM and ADaM data packages for submissions can be a daunting task. With all the guidance documentation and checks for files and data needed for submission, it's easy to get overwhelmed. This presentation will highlight essential documents to reference for submissions to both FDA and PMDA. A focus will be placed on key review items for traditionally non-automated checks, as well as critical cross- checks spanning both study data and submission documents to serve as a foundational resource. Attendees will be able to leverage this framework to create a more comprehensive checklist that will enhance their organization's own submission process | imputation, adam dataset, proc contents, sdtm domain, data cleaning, flag variable, adverse events, cdisc | source | 13 |
| 71 | Checking SDTM Datasets from Biostatistical Perspective | Inka Leprince, Elizabeth Li |
The adoption of Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) standards has significantly improved data consistency across the clinical research industry. However, conformance is not synonymous with suitability for statistical analyses. Compliance to CDISC standards does not guarantee that the necessary data elements are present or that they align with study- specific analytical requirements. It is crucial to additionally assess data quality, consistency, and alignment with analytical requirements to ensure accurate and reliable results. This paper explores five key considerations for evaluating the fitness of SDTM data sets for analysis, highlighting the importance of identifying and addressing data issues early. By integrating statistical review early in the data preparation process, organizations can enhance data quality, streamline analysis workflows, and improve the reliability of clinical study outcomes | data science, raw data, crf, sdtm domain, data cleaning, treatment arm, clinical trial, adverse events, concomitant medications, cdisc | source | 14 |
| 72 | Clinical Data Explorer: Transforming Clinical Data into Actionable Insights | Karma Tarap, Oriana Esposito, Maheshkumar Umbarkar, Ramnath Dhadage, Tamara Martin |
Early Development operates in a dynamic, high-stakes space where rapid, data-informed decisions are essential. With a "go fast, kill fast" approach, innovative study designs, continuous data monitoring, and dose optimization strategies drive efficient decision-making. Clinical Data Explorer (CDEx) is a near real- time (NRT) platform providing aggregated clinical and biomarker data through a self-guided, customizable interface. Designed for BMS OneClinical teams and other users, CDEx enables rapid visualization and statistical analysis of safety and efficacy trends, guiding data-driven decisions for dose escalations and expansions. The foundation of CDEx was built utilizing an R-shiny opensource package, Teal, created by Roche. CDEx integrates SDTM and ADaM lite data, ensuring consistent updates with automated refreshed of SDTM. Key benefits of CDEx include: Rapid access to clinical data for faster, informed decis ion-making | adverse events, clinical trial, clinical data, anova, cdisc | source | 6 |
| 73 | Clinical Data Quality Assurance: An Interactive Application for Data Discrepancy Detection | Yushan Wang | In today's data-driven clinical environment, ensuring the quality of data deliverable is essential for evaluating trial outcomes. This paper introduces an R Shiny application designed for statistical programmers to facilitate rigorous data discrepancy checks during programming and addresses frequent inquiries from a wide range of clinical stakeholders. The application streamlines the operational process by incorporating four key modules: Records with Missing Key Variables, Find Duplicates, Date Comparison, and Datasets Comparison. By integrating these functionalities into an user-friendly interface and incorporating robust R programming techniques alongside with R shiny framework, this application empowers statistical programmers to systematically address common data inconsistency and stakeholder inquiries encountered during programming and analysis/modelling processes | date variable, statistical programmer | source | 8 |
| 74 | Clopper Pearson CI? get your data ready for it! | Ruth Marisol Rivera Barragan, Isaac Vazquez |
The Clopper -Pearson method is a widely used approach for calculating exact confidence intervals for binomial proportions, particularly in scenarios requiring high precision. However, incomplete datasets often add challenges to this calculation, since we have data completion before confidence intervals can be accurately derived. This paper presents a way to use SAS and SQL code for completing missing data to enable the calculation of confidence intervals using the Clopper-Pearson method. The proposed approach automates data preparation, identifies and addresses missing values, and integrates confidence interval computation. Through practical examples, we demonstrate the accuracy and efficiency of the methodology, highlighting its applicability in statistical analyses with incomplete data. This work provides a valuable resource for researchers and practitioners working with binomial data in SAS | confidence interval, binomial distribution, system organ class, adverse events, clinical trial, trial elements, proc step, data science, proc freq, parameter, proc sql, sample | source | 7 |
| 75 | Code Smarter, Not Harder: The 5 C's of ChatGPT for the SASsy Professional | Charu Shankar, Kirk Paul Lafler |
In today's fast-paced analytics world, efficiency is everything. This paper explores how ChatGPT and SAS complement each other across the 5 C's to enhance productivity: Communicate - Automate structured email reports with SAS, or use ChatGPT for dynamic, polished messaging. Learn when to use each for maximum impact. Code - Speed up SAS programming with ChatGPT's syntax suggestions, debugging tips, and code generation-all while maintaining best practices. Collaborate - Use SAS for version control and shared repositories, while ChatGPT streamlines teamwork with summaries, documentation, and code explanations. Customize - Enhance efficiency with SAS macros and fine-tune ChatGPT prompts for tailored reporting and automation. Create - Harness AI-driven insights for problem-solving while ensuring accuracy and compliance in SAS analytics. Featuring hands-on demos and real-world examples, this session will equip you with practical strategies to code smarter, not harder | excel, proc step, dataset, proc sql, inner join, proc hpsplit, sample, nominal variable, create table, ods excel, correlation, output delivery system, proc freq, data cleaning, proc means, logistic distribution | source | 49 |
| 76 | Code Switching: Parallels Between Human Languages and Multilingual Programming | Danielle Stephenson, Laura Mino |
As multilingualism gains importance in the pharma industry, we've observed that the processes for learning programming languages closely mirror those for learning human languages. What insights can we draw from the study of natural languages to enhance our approach to multilingual programming? What similarities can we find between a SAS programmer learning R and a native Mandarin speaker learning English? Join us, two polyglots who enjoy both people-talk and code-talk, as we explore natural and programming languages to see how techniques for understanding one can be leveraged to understand the other | if-then-else, pharmaceutical industry | source | 10 |
| 77 | Combined Waterfall and Swimmer Plot using R for Visualization of Tumor Response Data | Akshata Salian | Visualizing tumor response data is crucial in oncology studies to assess the effectiveness of treatments and monitor patient outcomes. The waterfall plot typically displays the percentage change in tumor size from baseline for each patient and the Swimmer plot provides a detailed, patient -level representation of treatment timelines showing duration of treatment and clinical events over time. The combined visualization integrates key elements of both plots and offers a more comprehensive understanding of treatment outcomes. Tumor progression can be directly linked to the length of therapy, offering insights into how response evolves over time. It also facilitates comparisons between subgroups (e.g., treatment arms) by incorporating multiple data dimensions into the plot. It also reduces the need for multiple plots, making it easier to present and interpret data at first sight. In SAS, the option is available to combine them together for an effective interpretation | swimmer plot, waterfall chart | source | 13 |
| 78 | Common Issues in BIMO Clinical Site Dataset Packages | Michael Beers | When preparing a submission package for the Bioresearch Monitoring (BIMO) Clinical Site (CLINSITE) Dataset, it is important to ensure compliance to the FDA's specifications and guidance. It is often the case, however, that issues exist with the CLINSITE dataset and the associated documentation. This paper will review some of the most common issues seen across the industry, and how the issues should be addressed | safety population, treatment arm, missing value, adam dataset, data cleaning, clinical trial, cdisc | source | 9 |
| 79 | Comparing SAS and R Approaches in Reshaping data | Yachen Wang, Chen Ling |
Data reshaping is a fundamental process in data management. Both SAS and R offer robust capabilities to transform data between wide and long formats, enabling researchers to manipulate and reorganize datasets for effective analysis. This paper compares SAS PROC TRANSPOSE and R's pivot_longer() and pivot_wider() from {tidyr} package in data reshaping. Apart from their basic data transformation capabilities, both tools can customize variable names, handle duplications and deal with unused variables. SAS is more preferred while handling labels in data reshaping, due to its unique labeling feature. R, on the other hand, offers additional flexibility with its useful options, enhancing the ability to manage more complex data scenarios. Practical example codes in SAS and R are provided to illustrate these features and differences. These examples will help users understand the application of each method and demonstrate the strengths and limitations of each approach |
proc transpose, proc step, data restructuring, data science | source | 12 |
| 80 | Comparing SQL and Graph Database Query Methods for Answering Clinical Trial Questions with LLM-Powered Pipelines | Jaime Yan | Clinical trial data analysis presents a fundamental challenge: researchers need to extract insights from com- plex, interconnected datasets but often lack the technical expertise to write sophisticated database queries. This paper introduces RagQL-Nav (Retrieval Augmented Generation with Query Language Navigation), a multi-agent framework that bridges this gap by enabling natural language querying of clinical trial data across both SQL and Neo4j graph databases. We present a comparative analysis of these two database ap- proaches within the context of ADaM (Analysis Data Model) datasets[1, 2, 3], leveraging Large Language Models (LLMs) to translate natural language questions into executable queries. Our framework employs intelligent query routing, dual-system validation, and visual decision tree generation to optimize perfor- mance and ensure accuracy | data cleaning, clinical trial, proc sql, proc hpsplit, clinical data, adverse events, adam dataset, data science, cdisc | source | 24 |
| 81 | Conducting Survival Analysis in SAS using Medicare Claims as a Real-world data source | Jayanth Iyengar | Applications of Survival analysis as a statistical technique extend to longitudinal studies, and other studies in health research. The SAS/STAT package contains multiple procedures for performing and running survival analysis. The most well-known of these are PROC LIFETEST and PROC PHREG. As a data source, Medicare claims are often used in Real-world evidence studies and observational research. In this paper, survival analysis and the SAS procedures for performing it will be explored, and survival analyses will be conducted using Medicare claims data sets to assess patient's prognosis amongst Medicare beneficiaries |
proc step, cpu time, survival analysis, proc phreg, survival function, proc lifetest, hazard ratio, predictor variable, proc sql, insurance claims, chi-square test, dataset, nominal variable, censoring, rank test, likelihood function, proc sgpanel, exploratory data analysis, data science, probability distribution, binary variable, hazard function, log-rank test, lookup, maximum likelihood estimation, proc means, proc sgplot, proc sort, create table, if-then-else, wilcoxon test, regression analysis, proc freq | source | 32 |
| 82 | Considerations of R submission in Japan | Yuichi Nakajima, Yasutaka Moriguchi |
R adoption and submission are key topics in the pharmaceutical programming industry, and Japan is no exception. Although SAS users still constitute a large population in Japan, interest in R has grown significantly in recent years. Industry communities such as Japan Pharmaceutical Manufacturers Association (JPMA) and PHUSE have conducted surveys on R adoption and initiated communication with Pharmaceuticals and Medical Devices Agency (PMDA), the Japanese health authority. PMDA has shown openness to accepting R, and sponsors need to consider the best approaches for R submissions, including understanding PMDA - specific requirements (e.g., PMDA consultations, PMDA gateway submissions following folder structure guidelines, etc.). In this paper, we will briefly introduce the New Drug Application (NDA) process with PMDA and highlight key points for preparing PMDA communications and R submission deliverables | data cleaning, data validation rules | source | 5 |
| 83 | Controlling attributes of .xpt files generated by R | Yachen Wang, Chen Ling |
In the process of electronic data submission to Food & Drug Administration (FDA), the most common way is using SAS to generate Simplified Transport (.xpt) files. Currently, more and more companies are exploring using R to generate statistical outputs for regulatory submission, and programmers are trying to generate .xpt file in R. However, due to the software setup difference between SAS and R, the attributes of dataset and variables are not always handled very well. As we know, labels are crucial to SDTM and ADaM datasets, also labels, length and format are indispensable components of variables, which should follow CDISC standard. In this paper, we provide comprehensive guides about checking and assigning attributes to datasets (labels) and variables (length, labels and formats) while developing. xpt files in R. Moreover, we also introduce efficient ways to assign these attributes in R generated .xpt files based on specifications documents | adam dataset, sample, error message, label statement, cdisc | source | 13 |
| 84 | Creating JSON Transport Data for Regulatory Life Sciences | Mary Dolegowski, Matthew J. Becker |
The capacity to convert data into a JSON structure has become indispensable as the use of APIs in regulatory life sciences becomes more critical. This capability facilitates interoperability, data sharing, and conformance among systems. In this context, we illustrate the process of converting SDTM (Study Data Tabulation Model) data to the Dataset-JSON format, which is a critical prerequisite for regulatory submissions to agencies such as the FDA. We emphasize two SAS methods for this conversion: the CASL2JSON function in conjunction with PROC JSON and PROC CAS. Although Dataset-JSON is employed to illustrate these methods, the underlying processes are inherently data-agnostic, which allows for their application to other regulatory data structures. Seamless adaptation to changing industry standards and submission requirements is guaranteed by this adaptability |
proc step, proc json, sas viya, life sciences, cdisc | source | 6 |
| 85 | Cytokine Release Syndrome (CRS) - Data Collection, Clinical Database Integration and Analyses in a Dose Escalation Cell Therapy Trial | Cyrille Correia, Venky Chakravarthy |
Cell therapy is a promising new therapeutic option in treating cancer. It has the potential to treat patients in other therapeutic areas such as autoimmune diseases. However, Cytokine Release Syndrome (CRS) remains a significant safety concern in these clinical trials. This presentation details the integration of CRS data into clinical study databases and subsequent analyses. We emphasize the process from data collection to CDISC-compliant data sets. CRS data is collected via electronic Case Report Forms (eCRFs) and integrated into Study Data Tabulation Model (SDTM) data sets. We will focus on SDTM.AE/SUPPAE for adverse events, SDTM.CE/SUPPCE for CRS signs and symptoms, and SDTM.CM/SUPPCM for treating CRS with drugs like tocilizumab. The relationships between these data sets are mapped using SDTM.RELREC to ensure accurate linkage of CRS events, treatments, and symptoms | sample, dose escalation, ctcae grade, adverse events, clinical trial, clinical programming, concomitant medications, cdisc | source | 13 |
| 86 | Data management and biostatistics synergy: how to achieve and what can be expected | Diana Avetisian | Conduct of clinical trials can be a compilated process especially when it comes to data collection and statistical analysis. There are a lot of standards to consider for data collection, analysis and representation of the results such as CDISC standards and regulatory recommendation including different guidances for specific therapeutic area. To achieve high quality results, meet all expectations and have a successful study submission all functions have to work in sync. Each group have their own rules, limitations, and standards to consider so in order to succeed we have to find a way and right time to communicate and help one another to avoid any unnecessary rework. Specifically, when it is coming to data collection and analysis data management and biostatistics have to work together and understand each other needs | data governance, database lock, data validation rules, statistical programmer, data review, adverse events, clinical trial, medical coding, data science, data cleaning, clinical database, raw data, cdisc | source | 11 |
| 87 | Debugging Options in R: Applications and Usage in Clinical Programming | Madhusudhan Ginnaram, Bingjun Wang, Jeetender Chauhan, Sarad Nepal |
Debugging is a critical component of R programming, especially in the clinical data analysis where data integrity and accuracy are paramount. R, a programming language widely used for statistical computing and data analysis, provides a variety of debugging options that help developers identify and resolve coding errors effectively. This paper explores the various debugging options available in R and discusses their practical application. Additionally, we examine the significance of proper debugging in ensuring reliable statistical outputs, regulatory compliance, and overall data quality in clinical trials. By detailing the usage of these debugging options, this paper aims to enhance the understanding of R's capabilities in clinical programming and underscore the importance of effective debugging practices | clinical trial, change from baseline, clinical programming, statistical programmer, outlier, data science | source | 6 |
| 88 | Deciphering Exposure-Response Analysis Datasets: A Programmer-s Perspective for Oncology Studies | Sabarinath Sundaram | Exposure-Response (E-R) evaluation is essential in drug development and regulatory reviews by informing decision-making towards optimized trial design, dose and regimen selection, and benefit -risk assessments in both early and late-stage trials. Analyzing the relationship between drug exposure and treatment outcomes using E-R data provides a level of granularity to support the primary evidence of a drug's safety (identifying negative effects) and/or efficacy (positive effects). The preparation of high - quality E-R datasets is a key step in this space, which can get challenging especially in oncology studies which are quite complex and involve multiple factors and mechanisms. The creation of these E-R analysis datasets requires a comprehensive mix of data sources, including drug exposure, patient demographics, key covariates, PK/PD data, and key primary or secondary endpoints of the clinical trial | adam dataset, clinical trial, statistical programmer, adverse events, clinical research, exposure, objective response rate, progression-free survival, response variable, data science, concomitant medications, regression analysis, cdisc | source | 6 |
| 89 | Decoding Laboratory Toxicity Grading: Unlocking the Potential and Overcoming Challenges of CTCAE | Xiaoting Wu, Lei Zhao |
Toxicity grading is a critical tool for assessing the severity of adverse events and ensuring clinical trial safety. The Common Terminology Criteria for Adverse Events (CTCAE), developed by the National Cancer Institute (NCI), provides standardized criteria for grading adverse events. Originally designed for oncology, CTCAE has since been applied across various therapeutic areas. Despite its widespread use, significant challenges remain in interpreting and implementing CTCAE criteria, particularly in the context of laboratory toxicity grading. Existing publication provides limited guidance on these complexities, including the concepts of CTCAE for laboratory toxicity, the integration of CTCAE grading into analysis data models, and the application of CTCAE criteria to laboratory results at baseline or post-baseline | ctcae grade, clinical trial, adverse events, laboratory abnormality, shift table | source | 6 |
| 90 | Decoding the Role of Statistical Programming - A Decade of Keytruda Submissions and Approvals | Mary N. Varughese, Hong Qi |
The contribution of statistical programming (SP) to clinical development and subsequent regulatory submission of biopharmaceutical products is essential. Keytruda (pembrolizumab), a human PD-1 (programmed death receptor-1)-blocking antibody, was first approved by the US Food and Drug Administration (FDA) in September 2014. Since then, over 40 indications have received approval from regulatory agencies worldwide. Each of these approvals involves quite a complex planning and preparation process, during which SP acts as a catalyst to ensure the accuracy of all submitted documents and the efficiency of the process and responses to regulatory inquiries. This paper is intended to provide insights into the pivotal role of SP in the development and regulatory submission of Keytruda | regulatory submission, data standard, clinical trial, adam dataset, clinical data, data science, cdisc | source | 8 |
| 91 | Develop a SAS Macro for Dataset/Program Timestamp Version Control | Chen Wang | In the context of various clinical trial study deliverables, programmers who support study CSR, eDMC, etc., are tasked with managing a multitude of files, including programs, datasets, and Tables, Listings, and Figures (TLFs). Ensuring version control of these study files is increasingly crucial, presenting a growing challenge for the clinical study team in maintaining and tracking version control. The absence of a tool for checking version control can easily lead to issues such as copying or updating incorrect files. To address this challenge, this paper introduces a SAS macro that checks file consistency by comparing modified timestamps and produces a comprehensive list of files between the source and target directories, including their sub- directories. Implementing this macro will empower study programmers to effectively monitor file versioning and avoid potential errors. Keywords: SAS , Macro, analysis, version control | macro call, clinical trial, macro definition, proc sql | source | 4 |
| 92 | Does SDTM Validation Really Require Double Programming? | Sunil Kumar Gupta, Tomás Sabat Stofsel |
While double programming for SDTM validation is the gold standard, it may be outdated with advances in technology! The key purpose of double programming is not to duplicate efforts but to assure independent programming to reproduce the same SDTMs based on interpretations of common specifications and raw data. The outcome expected is to prevent false positive and false negative results as well as minimize the likelihood of coding errors or biases. The FDA requires that sponsors validate SDTMs, ADaMs and TLGs but not how to validate the results which means that 100% double programming is not required. In fact, FDA has guidelines for applying a risk-based approach to the validation process and for the use of AI to support clinical trials development. This article will explore the concept of double programming and alternative methods to validate SDTMs using technology as well as sponsor oversight management | independent qc, data cleaning, raw data, clinical trial, missing value, controlled terminology, macro definition, sas programmer, proc freq, adverse events, cdisc | source | 5 |
| 93 | Efficacy Endpoints Related to CNS in Oncology Studies within ADaM | Lihui Deng, Kylie Fan |
In oncology trials, RECIST 1.1[1] serves as the international standard for assessing disease response in patients. However, in recent trials of targeted therapies-such as those for non-small cell lung cancer (NSCLC)-specialized response patterns related to the central nervous system (CNS) have been observed. These include hypo-responsiveness or hyper-responsiveness in CNS versus extra-CNS regions. Consequently, CNS response can significantly impact the widely used endpoints derived from | objective response rate, best response, duration of response, adam dataset, efficacy summary, randomization, progression-free survival, time to progression, survival analysis, clinical trial, cdisc | source | 6 |
| 94 | Efficiency Techniques in SAS 9 | Stephen B. Sloan | Using space and time efficiently has always been important to organizations and to programmers in general and to SAS programmers in particular. We want to be able to use our available space without having to obtain new servers or other hardware resources, and without having to delete variables or observations to make the SAS data sets fit into the available space. We also want our jobs to run more quickly both to reduce waiting times and also to ensure that scheduled job streams finish on time and that successor jobs are not unnecessarily delayed. Internal mainframe billing algorithms have always rewarded efficiency. As we move toward cloud computing efficiency will become even more important because the billing algorithms in cloud environments charge for every byte and every CPU second, putting an additional financial premium on efficiency | proc step, character variable, numeric variable, dataset, proc means, proc sort, by group processing, missing value, proc contents, proc freq, sas log, length statement, proc sql, rename statement | source | 9 |
| 95 | Efficient CDISC Controlled Terminology Mapping: An R-Based Automation Solution | Yunsheng Wang | This paper presents a method for automating the generation of SDTM Controlled Terminology (CT) terms using an R Shiny application. The system connects customized CT library to map EDC raw terms to their corresponding SDTM CT terms. Using similarity-based matching algorithms, the system ensures that all EDC raw terms are auto mapped to corresponding SDTM CT terms. When exact matches are not found, the web-based interface allows users to review and modify the suggested CT terms to align with sponsor requirements. The tool supports the export of terms such as datasets, EXCEL files, or program code. The paper demonstrates methodology, system architecture, and practical applications, highlighting the tool's potential to improve clinical data management, data visualization, and data standardization in clinical trials |
cdisc controlled terminology, edc, raw data, excel, cdisc | source | 8 |
| 96 | Efficient Strategies for Handling Data Issues in Clinical Trial Submissions | Kate Sun | Clinical trial data is often complex and prone to issues such as missing data, outliers, and inconsistencies. Efficient identification and resolution of these issues are critical to maintaining data integrity and ensuring successful regulatory submissions. This paper presents a professional SAS framework to proactively identify and address data anomalies in the SDTM.LB domain. By utilizing data visualizations, macros, and practical SAS techniques, this approach offers a systematic solution to enhance data quality and streamline the clinical trial submission process. The paper includes SAS code examples, output visualizations, and guidance for statistical programmers to practice | clinical trial, imputation, outlier, missing value, clinical data, mean imputation, data science, sample, proc sql, proc freq | source | 7 |
| 97 | Efficiently Creating Multiple Graphs on One Page utilizing SAS: Comparing PROC GREPLAY and ODS LAYOUT Approaches | Jenny H. Zhang | In clinical research, programmers often need to produce a variety of graphs as part of data analysis. Generating multiple graphs on one page is a challenging task. This paper will compare two different methods of creating multiple graphs on a single page, highlighting their advantages and limitations. The first method examines traditional techniques such as employing PROC GPLOT followed by PROC GREPLAY to arrange the graphs. This old approach often results in a complex workflow that requires users to manage separate graphics and can lead to challenges in displaying the outputs as desired. The second method adopts a modern approach, leveraging the capabilities of ODS LAYOUT in conjunction with PROC SGPLOT. This contemporary method simplifies the procedure for generating the multiple graphs on one page where users can easily integrate their graphs into various ODS output formats (HTML, PDF, RTF, etc.) without requiring any additional steps |
proc step, proc greplay, data visualization, proc gplot, proc sgplot | source | 8 |
| 98 | Elevating Clinical Research: Strategic Implementation of CDASH and SDTM Standards | Hayden Patel | In the ever-evolving landscape of pharmaceutical research and clinical trials, harmonizing data standards is crucial for efficient and reliable data management. This paper presents a comprehensive strategy for implementing Clinical Data Acquisition Standards Harmonization (CDASH) and Study Data Tabulation Model (SDTM) to standardize clinical data management. Clinical research involves extensive data collection, necessitating high data quality, traceability, reusability, and cost-effectiveness. CDASH and SDTM standards serve as a common language for data exchange and reporting, facilitating seamless communication among stakeholders. Effective governance is ensured through the Governance Team, consisting of members from key functional areas responsible for CDASH and SDTM compliance. The three-phase solution includes standardizing Case Report Forms (CRFs), with a focus on Safety CRFs and Therapeutic Area (TA)-specific CRFs, using CDASH and associated metadata | clinical research, controlled terminology, data cleaning, phase i, data standardization, cdisc | source | 4 |
| 99 | Embracing an Extra Step as the Ultimate Shortcut: Leveraging ADDATES for Enhanced Efficiency and Traceability in Oncology Studies | Chia-Lu Lee, Wanchian Chen, Anna Chen |
In oncology efficacy analysis, figuring out RECIST criteria can be like solving a complex puzzle. It involves integrating various data points, such as the sum of target lesion sizes, non-target lesion responses, and the presence of new lesions. That's where Event Dates Analysis Dataset (ADDATES) comes in, an approach first suggested in the CDISC Prostate Cancer TAUG back in 2017, to improve traceability and transparency. This paper illustrates how ADDATES connects existing tumor result and visit response datasets, simplifying the process of deriving critical dates and supporting endpoint analyses in ADEFF and ADTTE. It provides a step-by-step guide with examples for linking ADaM datasets using ADDATES, ideal for programmers and statisticians working on oncology studies, particularly those building efficacy endpoints based on RECIST criteria from scratch | adam dataset, efficacy summary, progression-free survival, time-to-event endpoint, cdisc | source | 6 |
| 100 | Embracing Continuous Learning in the Life Sciences Ecosystem | Iuliana Constantin | In our high-tech era, continuous training and upskilling have never been more crucial. We are fortunate to have a wide array of options available, whether you're a trainee or an employer. These include online or in-person certifications, workshops, and webinars, some of which are free, while others come at a cost. Numerous professional non-profit and for-profit organizations offer these training opportunities. However, with the abundance of options, selecting the right training becomes a challenge. It's essential to consider which training aligns most closely with the business needs of the company or the professional growth of an individual. Factors such as cost, training duration, quality, and expected outcomes play a significant role in this decision-making process. In my research on training, I have identified several options that could be beneficial to the life sciences ecosystem, including apprenticeships, co-ops, upskilling, and certifications | clinical research, life sciences | source | 11 |
| 101 | Empowering the Next Generation of Professionals: Merck-s Approach to Rising Talent Engagement and Leadership Development in Statistical Programming | Aston Smith, Sarah Alavi, Jeff Xia, Tarak Patel |
According to research from 2023, 40% of business leaders think recent graduates are not prepared to enter the workforce (Tenore, 2023) . To create a workplace environment supportive of the growth of rising professionals, the pharmaceutical industry must proactively implement targeted talent development initiatives that equip these emerging professionals with the necessary skills, knowledge, and leadership capabilities to drive innovation and success in statistical programming. This paper aims to examine Merc k's BARDS Statistical Programming Rising Professionals Club, an innovative talent development program designed to engage and nurture rising professionals within our statistical programming organization. In addressing these challenges, the Rising Professionals Club offers a multi - faceted a pproach, equipping our rising talent with the skills and knowledge essential for success in statistical programming and future leadership roles | clinical programming, pharmaceutical industry, statistical programmer | source | 7 |
| 102 | Enhance Safety Data Analysis Using SafetyGraphic R Package | Vicky Yuan | Safety data analysis presents numerous challenges in both interpretation and evaluation in clinical trial. Traditionally, safety data analysis has relied on tabular summaries and statistical tests to detect potential safety signals. such as generating Adverse Event (AE) tables and listings, summary of laboratory statistics, and monitoring key parameters of vistal signs and ECG. Those make it difficult to detect complex patterns. sometimes large volumes of static data can lead to delayed signal detection. {SafetyGraphic} package play a crucial role in improving the communication of safety results to regulators, investigators, data monitoring committees, and other stakeholders. Visual analytics can present complex safety data more concisely and effectively than tables and listings, facilitating a clearer understanding of potential safety concerns | adverse events, clinical trial, outlier, data science, demographics table | source | 17 |
| 103 | Enhance your Coding Experience with the SAS Extension for VS Code | Jim Box | Visual Studio Code (VS Code) from Microsoft is an open-source code editor that is very popular among developers for its ease of use across all programming languages which is driven by a robust extension ecosystem. The SAS VS Code extension is an open-source, freely available add-on that allows you to use VS Code to connect to any modern SAS Environment, from SAS 9.4 on your local machine to SAS Viya in the cloud. The key features include Syntax Highlighting, Code Completion, Syntax Help, Data Viewer, and my favorite, SAS Notebooks, which offer an exciting way to share content and comments. We'll look at the extension, how to use it, and explore how you can get involved with the direction of how this product evolves | sas 9, sas viya | source | 10 |
| 104 | Enhancing Clarity and Efficiency in Clinical Statistical Programming Task Management: An Automated Integrated Task Reporting Solution with Excel and Python | Xinran Hu | Effective task management and timely communication are critical in clinical statistical programming, where multiple studies, tight timelines, and cross-functional collaboration converge. This paper presents an integrated Python-based solution to automate task reporting across weekly, monthly, quarterly, and yearly intervals. By extracting data from an EXCEL-based task tracking tool, the system categorizes tasks based on due dates (past week, current week, upcoming week, and quarterly milestones) and priority levels. Personalized emails are automatically generated and scheduled for delivery to team members and leaders at predefined intervals-such as every Monday at 9 AM, the first day of each month/quarter/year, or at the end of a reporting period for summary analysis. For team leaders, the system provides a consolidated report highlighting high-priority milestones along with an analytical summary of task completion rates, categorized by team members, study, project, or compound |
excel, clinical programming | source | 10 |
| 105 | Enhancing Dictionary Management and Automation with NCI EVS: A Deeper Dive for the CDISC Community | Anthony Chow | For many in the CDISC community, the US National Cancer Institute's Enterprise Vocabulary Services (NCI EVS) may primarily serve as a source for downloading CDISC Controlled Terminology (CT) files or setting data submission values. Yet, this view underestimates the critical, long-standing partnership between CDISC and NCI EVS in developing semantic frameworks that underpin CDISC's standards. Through advanced semantic management tools, NCI EVS maintains CDISC's codelists and controlled terms in a structured and automated environment-far beyond the spreadsheet-based systems. Thanks to a comprehensive API, users can readily access these resources without navigating complex backend processes. This paper will explore two core NCI EVS tools that can support and enhance CDISC workflows: the EVS Explore, an interactive tool for concept browsing, and the API driving this robust application. We will highlight practical use cases that illustrate their value to standards professionals | cdisc controlled terminology, controlled terminology, rest api, clinical data, clinical research, data standard, data cleaning, cdisc | source | 6 |
| 106 | Enhancing Health Equity Outcomes through Comprehensive Data Collection of Marginalized Populations including Sexual Orientation, Gender Identity and Intersex Status (SOGI) | Donna Sattler | The collection of comprehensive data from marginalized populations is essential for advancing health equity in clinical trials. The LGBTQIA+ community has faced significant healthcare shortcomings over the years, largely due to discrimination, lack of education among healthcare providers, and systemic inequities leading to underrepresentation from these populations, causing disparities in health outcomes and access to medical advancements | clinical trial, laboratory data | source | 5 |
| 107 | Ensuring Data Integrity: Techniques for Validating SDTM Datasets in Clinical Research | Amy Welsh | In clinical research, programmers often encounter studies where the Study Data Tabulation Model (SDTM) data sets are already complete. In such scenarios, the newly assigned programmer is tasked with ensuring the reliability and accuracy of these data sets. This paper explores various techniques, adapted to help the programmer confirm the integrity of SDTM data sets. The methods discussed will: 1. Provide an overview of the study data. 2. Identify and address unexpected values within the data sets. 3. Detect missing values or variables. 4. Facilitate the rapid tracing of raw data through the SDTM process. 5. Ensure that all records from the raw data are accurately represented in the SDTM data sets. By employing these techniques, the newly assigned programmer can confidently ensure the data they are now responsible for is reliable | proc freq, raw data, proc step, missing value, clinical research, record count, data cleaning, proc sql, cdisc | source | 8 |
| 108 | Essential Elements for an Effective New-Hire Training Handbook | Ingrid Shu, Xinhui Zhang |
New hires entering the pharmaceutical industry possess the technical skills required to EXCEL, but if industry-specific concepts and workflows are not adequately introduced during onboarding, the learning curve may remain steep. This results in new hires struggling to unlock their full potential and experiencing hardship in deriving meaning from their work. To reduce growing pains for both mentors and new hires, teams can adopt an informal training handbook that pays special attention to their team's unique processes. This handbook does not replace official resources; instead, it complements them, using simplified language and presentation specifically tailored for programmers transitioning into their role for the first time. To facilitate effective onboarding, the handbook should be aligned with 3 key principles: centralization, relevance, and foundational knowledge |
statistical programmer, clinical programming | source | 7 |
| 109 | Evaluation of the Process to Create CLINSITE define.xml: Macro Approach vs. ADCLIN Spec | Yizhuo Zhong, Yunyi Jiang, Christine S Teng |
The Bioresearch Monitoring (BIMO) program is an initiative established by the U.S. Food and Drug Administration (FDA). This program conducts inspections and audits of clinical trial sites, sponsors, and contract research organizations (CROs) to ensure compliance with Good Clinical Practice (GCP) regulations. Within the BIMO submission package, Clinical Site Data (CLINSITE) pertains to the information gathered at specific locations where clinical trials are carried out. The define.xml file for the CLINSITE dataset is a crucial deliverable that describe the key information collected from each site. To streamline the process of preparing the BIMO package, this paper will assess two proposed approaches using P21E Enterprise (P21E) to generate the CLINSITE define.xml file. The first approach involves employing a SAS macro to transform sponsor-specific CLINSITE specifications into the latest P21E export format. This macro will include specific input parameters | sample, macro parameter, data cleaning, cdisc | source | 8 |
| 110 | ExCITE-ing! Build Your Paper's Reference Section Programmatically Using Lex Jansen's Website and SAS | Louise S. Hadden | One challenge in writing a SAS White Paper is creating the perfect reference section, properly acknowledging those who have inspired and paved the way. Luckily, clever use of such tools as Lex Jansen's website, SAS's ability to read in and manipulate varied data sources, and Microsoft Word citation manager, every author can succeed in proper referencing in their white papers. This paper and ePoster will demonstrate how to accomplish this goal | source | 9 | |
| 111 | Exploring the Upcoming Integrated cSDRG! | Srinivas Kovvuri, Christine McNichol, Randi McFarland, Kiran Kumar Kundarapu, Satheesh Avvaru |
The Integrated Summary of Safety (ISS) and Integrated Summary of Efficacy (ISE) are essential for the approval of New Drug Applications (NDAs) and Biologics License Applications (BLAs). Sponsors must submit data supporting these integrated analyses and a Reviewer's Guide. There are multiple strategies to create integrated analysis datasets. One strategy is first to develop integrated Study Data Tabulation Model (CDISC SDTM) datasets to use as the source to build integrated analysis datasets. To facilitate the documentation of integrated SDTM, the PHUSE Optimizing the Use of Data Standards Working Group has developed a template for the integrated clinical Study Data Reviewer's Guide (icSDRG). An example document has been created to clarify the use of this template to meet regulatory agencies' (RA) recommendations and ensure consistent data submissions across the industry | adam dataset, clinical trial, cdisc | source | 13 |
| 112 | Follow the Yellow Brick Road: Finding the Critical Path for a Study-s Lifespan | Jake Gallagher | Imagine how confusing the Wizard of Oz would have been if Dorothy didn't have the yellow brick road to follow. What if she found the Wiz before her other friends? She might not have gotten back home to Kansas! When a study is assigned to a lead programmer, a plethora of tasks has now been placed on their laps. Documents to fill out, timelines to create, programs to make (oh my!) - all these items have a due date, but in what order should they be done? Should documents be made first, then programs to follow suite? Or should outputs be the focus, documenting as you go? Without a critical path to follow, a lead programmer can easily become overwhelmed when looking at their to-do list, falling into roadblocks and inefficiencies. This paper outlines the critical path analysis in a study's lifespan to streamline task execution and reduce potential bottlenecks to have the most efficient path toward study completion | clinical research, raw data | source | 4 |
| 113 | Four roads lead to Outputting Datasets Dynamically (ODDy) & Beyond | Jason J. Su | Outputting Datasets Dynamically (ODDy) is a common programming task, which is to partition an input table based on its contents on-the-fly. Although WHERE statement can be used to create subset datasets individually, the variable values to be used in the statement must be known beforehand, and the method is not quite efficient itself. The most efficient way might be to create all with just one-passing of the source or master one with OUTPUT statements. Then the question is: How to automate the whole process and create these OUTPUT statements dynamically? Here three popular methods are provided: MACRO, FILENAME+%INCLUDE, and CALL EXECUTE routine. Additionally, the paper gives two variants of the 1st solution, namely a macro program and a macro function. Finally, besides these generalized solutions, there is a special solution especially for ODDy, which is to output with HASH object |
call execute, output, macro variable, macro function, put statement, where statement, data step, character functions, proc step | source | 10 |
| 114 | From Null to Notable: Creating a Brand on Social Media | Inka Leprince | Navigating social media as a programmer requires a blend of technical knowledge, personal branding, and community engagement. The foci of this presentation are the best social media practices and strategies for enhancing visibility through tagging individuals/companies, establishing regular posting schedules, and leveraging fun and interactive content. Resources for free design software will be provided as well as practical tips for integrating branding elements in the creation of visually striking graphics. By following these guidelines, you can effectively promote yourself and engage your followers | uniform distribution, service provider | source | 7 |
| 115 | From Tides to Transformation: Making Waves with Your Move to Viya | Tania Morado, Morgan Halleen |
Transitioning from SAS 9 to SAS Viya can seem complex, with uncertainties around resource availability, planning, and success on the new platform. However, with the right support this change becomes an opportunity for growth. This paper will explore how Customer Success partners with organizations to ease the transition by creating structured success plans, offering abundant resources, and addressing common concerns to drive adoption and maximize value. Through collaboration and strategic guidance, we help organizations confidently navigate the move to Viya and unlock its full potential | sas 9, sas viya | source | 5 |
| 116 | Gen AI Assisted Code Conversion: From SAS to R Standard ADaM Templates | Jeff Cheng, Srinivas Malipeddi, Gurubaran Veeravel, Jaime Yan, Suhas R. Sanjee |
Generative AI (GenAI) holds significant promise for streamlining the conversion of code between programming languages. Given the recent shift towards open -source languages in clinical trial Analysis & Reporting (A&R), we evaluated GenAI's ability to convert an ADaM (Analysis Data Model) standards library from SAS to R. Our global standards library includes template programs in SAS that serve as starting points for creating ADaM datasets and involve minimal SAS macro code. Thus, we focused on using GenAI to convert these SAS -based ADaM templates to R, with the aim of providing study programmers hands -on experience in validating ADaM datasets with R. This transition also facilitates statisticians' review and understanding of ADaM dataset creation, as they are generally more comfortable with R | adam dataset, clinical trial, macro definition, data cleaning, clinical data, macro variable, statistical programmer, cdisc | source | 11 |
| 117 | Generating Synthetic Clinical Trial Data with AI: Methods, Challenges, and Insights | Max Ma, Weijie Yang, Emily Ma |
In the clinical trial industry, the ability to generate realistic synthetic data is essential for programming preparation, validation, data simulation, and the site EDC usage training. Artificial Intelligence (AI) offers powerful tools to achieve this by generating structured and meaningful synthetic data that aligns with predefined specifications. This paper discusses the use of Python and OpenAI's API to generate synthetic clinical trial data, utilizing the Pydantic library to enforce structured outputs. A comprehensive workflow is presented, covering the entire process from specification preparation to exporting data for Electronic Data Capture (EDC) systems or further analysis. All the examples referenced in this paper can be accessed at the following GitHub repository: https://github.com/Emily93643/AI-Synthetic-Data | clinical trial, adverse events, clinical data, structured data, data cleaning | source | 16 |
| 118 | Generative AI in Biometrics: Transforming Clinical Trials with Supercharged Efficiency an Innovation | Kevin Lee | The pharmaceutical industry stands at the beginning of a transformative era, with Generative AI (e.g., ChatGPT) revolutionizing clinical trial development. The paper will explore the integration of Generative AI in Biometrics, highlighting its potential to streamline workflows, redefine clinical trial development, and lead innovation. The paper will start with the introduction of Generative AI and its wide-ranging applications in Biometrics, such as information query, codes generation (e.g., SAS, R & Python), codes conversion (e.g. SAS to R/Python), document generation (e.g., SAP, CSR), data analysis, data visualization, patient profiling and many more. The paper will also explore the tools, systems, processes and people that are reshaping clinical trials with Gen AI integration. Looking toward the future, the paper will evaluate the lasting impact of Generative AI on Biometrics department | clinical trial, phase iii, data cleaning, data science, adverse events, pharmaceutical industry, secondary endpoint, clinical research | source | 6 |
| 119 | Geocoding with the Google Maps API: Using PROC FCMP To Call User-Defined SAS and Python Functions That Geocode Coordinates into Addresses, Calculate Routes, and More! | Troy Martin Hughes | Software interoperability describes the ability of software systems, components, and languages to communicate effectively with each other, and must be prioritized in today's multilingual and open-source development environments. PROC FCMP, the SAS Function Compiler, enables Python functions to be wrapped in (and called from) SAS user -defined functions (and subroutines), and the full panoply of SAS environments supports FCMP-including SAS Display Manager, SAS Enterprise Guide, SAS Studio, SAS Viya, and the latest Cary show pony, SAS Workbench . Productivity and the pace of development are maximized when existing open-source code-such as Python user-defined functions-can be run natively from a Python interactive development environment (IDE) rather than having to be needlessly recoded into the Base SAS language |
proc fcmp, clinical trial, sas function, proc step, output | source | 14 |
| 120 | Get Started with ChatGPT and DeepSeek in SAS | James Austrow | If you've been feeling like a wallflower at the LLM party, this paper is for you. Whether you're intimidated by the jargon, a SAS programmer marooned in a sea of Python tutorials, or simply don't know where to begin, start here. Follow along with this walkthrough to build, from scratch, the foundations of an AI-powered SAS application. You'll get properly acquainted with the language model lingo: prompt engineering, zero -shot, few-shot, chain-of-thought, and prompt chaining; all receive practical treatment. Don't just have them explained, see how you can apply them to solve a real data processing problem. The AI landscape is constantly evolving. Two of the most well-known language model services, ChatGPT and DeepSeek, receive specific focus here, but the concepts are generalizable and apply to all the major providers. Advanced SAS users stand to get the most out of this paper, but anyone, even non -programmers, with an interest in prompt engineering can benefit as well | imputation, proc print, proc step, macro definition, macro variable, proc http, macro quoting function | source | 17 |
| 121 | Get Tipsy with Debugging Tips for SAS Code: The After Party | Lisa A. Mendez, Richann J. Watson |
You've spent hours crafting your SAS code, or perhaps you've inherited a jumbled mess from someone else, and when you run it, it "explodes" like an ill-fated cocktail experiment and leaves a bad taste in your mouth! Mastering the art of debugging not only makes coding a smoother experience but also spares you from needing a stiff drink to cope with the frustration. This paper will discuss tips, tricks, and techniques for debugging your SAS code. It will show you how to use the debugger in both SAS Display Manager and SAS Enterprise Guide, akin to a bartender knowing just the right tools to mix the perfect drink. It will also describe how to use PUTLOG and PUT statements, along with their key differences-like choosing between a whiskey neat and a whiskey sour. Additional debugging aids such as using OPTIONS, and macro variables will be discussed, ensuring you have a well-stocked bar of techniques at your disposal |
sas enterprise guide, error message, put statement, macro variable, missing value, sas log, mlogic, macro facility, if-then-else, mprint, libname statement, macro definition | source | 16 |
| 122 | Getting under the "umbrella" of Specimen-based Findings Domains! | Soumya Rajesh, Kapila Patel |
The Study Data Tabulation Model Implementation Guide (SDTM IG) 3.4 has brought together domains that collect observations based on tests or examinations performed on collected biological specimens under one umbrella called "Specimen- based Findings Domains". They include Biospecimen Findings (BS), Cell Phenotype Findings (CP), Genomics Findings (GF), Immunogenicity Specimen Findings (IS), Laboratory Test R esults (LB), Microbiology and Pharmacokinetic domains. Each domain is defined to group measures of a common topic (e.g., microbiology susceptibility, microscopic findings, pharmacokinetic concentrations). This paper presents a comprehensive guide on how to determine the appropriate domain for various specimen-based findings. By examining the specific variables and criteria outlined in SDTM IG 3.4, we offer practical insights and examples to help programmers accurately categorize and report their findings | sample, sdtm domain, cdisc | source | 11 |
| 123 | Going from PROC SQL to PROC FedSQL for CAS Processing - Common mistakes to avoid. | Vijayasarathy Govindarajan | SAS 9 customers are increasingly looking at moving to SAS Viya to harness the power of the new distributed, in-memory, Cloud Analytic Services (CAS) engine. This often helps to speed up existing processes many times over and run analytics on huge datasets faster. One of the key areas of this migration involves updating SAS 9 PROC SQL code to take advantage of the processing capabilities of CAS. This is made possible by a new(er) procedure in the SAS arsenal: PROC FEDSQL. There are many differences between PROC SQL and PROC FEDSQL for CAS, from supported data types, available functions, applying formats, quoting strings, referencing macro variables etc. In my experience, users new to SAS Viya often make mistakes while migrating code to FedSQL which arise from a few basic misconceptions. This paper aims to clarify the key differences between PROC SQL and PROC FEDSQL for CAS. It will also highlight common mistakes when adapting SQL code for CAS, offering guidance on how to avoid them |
proc step, proc sql, sas viya, sas 9, create table, select, macro variable, drop table, order by, libname statement, parallel processing, put statement | source | 15 |
| 124 | Guidance from the FDA authored "Submitting Patient-Reported Outcome Data in Cancer Clinical Trials" and recommendations for their implementation in study CDISC COA data. | Charity Quick | In November of 2023, the U.S. Food & Drug Administration (FDA) released a 48- page document titled "Submitting Patient-Reported Outcome Data in Cancer Clinical Trials". This document provides technical specifications for submitting patient-reported outcome (PRO) data collected in cancer clinical trials to support oncology studies. Given the breadth and specificity of the guidance, the increasing prevalence of Clinical Outcome Assessments (COAs) in the work we do, and the direct link of the content to the FDA publications such as the "Study Data Technical Conformance Guide", adoption of the standards contained in "Submitting Patient-Reported Outcome Data in Cancer Clinical Trials" should be considered regardless of the COA therapeutic area. This paper and the accompanying poster seek to provide a summary of these FDA recommendations as they relate to study data collection, tabulation, analysis, and submission | missing value, intercurrent event, cdisc controlled terminology, clinical trial, secondary endpoint, raw data, adam dataset, clinical endpoint, proc step, cdisc | source | 14 |
| 125 | Handling Health Regulatory Information Requests: Best Practices and Strategies | Himanshu Patel, Chintan Pandya |
Addressing Information Requests (IRs) from health regulatory agencies such as FDA, EMA, MHRA, PMDA, etc., is critical during clinical trials. These agencies send information requests (IRs) for clarification, additional information, or verification of the data collected during the trial. For statistical programmers, responding to these IRs requires a detailed, step-by -step process, including information collection, resources and timeline planning, validation activities, maintaining documentation, and tracking all requests. This paper explores the statistical programming team's roles in handling IRs, exploring best practices and strategies for ensuring accuracy, efficiency, and compliance throughout the IR process, from information gathering to final submission. It helps programmers understand the technical and procedural aspects of information requests (IRs) and provides insight into navigating the best practices and strategies to enhance productivity and accuracy | data cleaning, statistical programmer, clinical trial, data integrity, independent qc, data science, adam dataset, clinical programming, missing value, cdisc | source | 11 |
| 126 | Handling Missing Data in External Control Arms: Best Practices, Recommendations, and SAS Code Examples | Yutong Zhang | External control arm (ECA) studies provide an alternative approach when traditional randomized controlled trials (RCTs) are not feasible. However, missing data in ECA are facing a critical challenge due to heterogeneous data sources, unstructured visit schedules, and the absence of randomization. Thes e factors increase the likelihood of Missing Not at Random (MNAR), which can undermine the robustness of treatment effect estimates if not properly addressed. This paper provides a practical review of missing data handling strategies in ECAs, including guidance on selecting appropriate methods based on the missingness mechanism. We discuss mixed models for repeated measures (MMRM), while emphasizing the flexibility and regulatory acceptance of multiple imputation (MI) under the Missing at Random (MAR) assumption. We also introduce sensitivity analysis techniques under MNAR, that are essential for evaluating the robustness of findings | imputation, missing value, sensitivity analysis, missing not at random, multiple imputation, treatment effect, randomized controlled trial, proc mi, clinical trial, proc mixed, proc step | source | 6 |
| 127 | Hands-on Training: eTFL Portal & TFL Designer Community | Bhavin Busa | With the introduction of CDISC Analysis Results Standards (ARS) and Analysis Results Data (ARD), the industry is moving toward greater standardization and automation in Tables, Figures, and Listings (TFLs). This hands-on training session will introduce participants to the eTFL Portal, a CDISC-hosted repository launched in October 2024 that provides examples and guidance for implementing ARS, and the Community version of TFL Designer, which was used to generate machine-readable mock-up shells and ARS metadata for eTFL artifacts. The eTFL Portal serves as a reference hub, offering standardized TFL templates, ARS metadata artifacts, and implementation examples to help organizations operationalize CDISC standards. Attendees will explore how to leverage these resources to align their workflows with ARS and ARD | adam dataset, regulatory submission, change from baseline, system organ class, clinical trial, clinical data, adverse events, cdisc | source | 8 |
| 128 | Harnessing AI & CDISC ARS for Effortless Statistical Reporting & CSR Writing | Bhavin Busa, Navin Dedhia |
Imagine a future where writing Clinical Study Reports (CSRs) is faster, more accurate, and nearly effortless, this is the promise of a Gen-AI-driven model leveraging CDISC Analysis Results Standard (ARS). We present a model fine-tuned on ARS and Analysis Results Data (ARD) metadata, streamlining the interpretation of analysis results and generating narrative summaries for CSR safety and efficacy sections. By processing pre-structured ARD datasets, the model identifies key findings and produces detailed summaries, significantly reducing manual effort. This approach enhances efficiency, saving time for biostatisticians, clinicians, and medical writers, while ensuring accuracy and consistency across TFL outputs and CSR text. We also discuss building this solution's pros and cons, including scalability, regulatory alignment, and data privacy challenges. By feeding in template CSRs, ADaM datasets, and ARDs, we were able to train the model to write CSR sections | clinical trial, adam dataset, clinical data, proof of concept, data cleaning, data science, regulatory submission, privacy, subject-level data, adverse events, cdisc | source | 10 |
| 129 | How Not to Drown in Code When Pooling Data | Scott Burroughs | At some point in our pharma careers, we'll likely have to pool some data from multiple studies. Could be for | macro variable, character variable, keep statement, proc contents, proc step | source | 5 |
| 130 | How Not to SAS: Avoiding Common Pitfalls and Bad Habits | Melodie Rush | Using SAS software effectively isn't just about knowing the syntax-it's about developing habits and strategies that enhance your efficiency and accuracy. In this 20-minute session, we'll take a humorous look at some of the worst practices in SAS programming, curated with the help of ChatGPT, and explain why these missteps can lead to frustration, inefficiency, or even disastrous results. By highlighting these "bad tips," we'll demonstrate best practices to avoid these mistakes, organize your work, optimize performance, and debug like a pro. Whether you're a beginner or an experienced SAS user, this session will give you practical advice to sharpen your skills and streamline your SAS journey | macro variable, sysdate9, date variable, proc step, datepart, sas log, sas enterprise guide, permanent library, macro definition, sas studio, likelihood function | source | 10 |
| 131 | How to compute common clinical trials statistics in R | Oleksandr Babych | Nowadays, R has already become an indispensable tool for clinical trial analysis. Yet, many clinical programmers struggle to learn R or try to avoid it at all. In addition, the growing number of R packages offering equivalent computations could make the learning process even more confusing. This paper aims to fill in the gap on how to calculate various statistics in R and help you acquire such a necessary skill. In this article, we would like to discuss different R packages and show how they can be used to compute various statistics, starting from basic descriptive statistics to more complicated and vital efficacy ones such as p-value, hazard ratio with confidence intervals, odds ratio, etc. Moreover, we will look at different | confidence interval, clinical trial, proc means, hazard ratio, odds ratio, survival analysis, treatment arm, binomial distribution, regression analysis, log-rank test, rank test, proc phreg, confidence level, datepart, median survival, proc step | source | 15 |
| 132 | How to get your SAS-Python-R workout on a new SAS Viya Workbench. | Pritesh Desai, Samiul Haque |
SAS Viya Workbench represents a significant leap forward in analytics and IT infrastructure by seamlessly integrating SAS programming with open-source frameworks. This paper explores how SAS Viya Workbench enhances efficiency in IT environments, fosters collaboration between SAS and open- source developers, and accelerates analytics development. Additionally, it outlines the roadmap and future advancements, highlighting the transformative impact of this technology | sas viya, sas studio | source | 9 |
| 133 | Identification of Domains Containing Screen Failure Participants in SDTMs and ADaMs for Reviewer-s Guides | Sumanjali Mangalarapu | This paper addresses the challenge of efficiently identifying screen failure participants within submission datasets, as outlined in the Clinical Submission Data Reviewer's Guide (CSDRG) and the Analysis Data Reviewer's Guide (ADRG). Statisticians and programmers often face time-consuming manual reviews of numerous SDTM and ADaM domains to pinpoint screen failure participants. The popular software, Pinnacle 21 community version, which performs compliance checks, cannot trace screen failure in all SDTM/ADaM datasets. This paper presents a programmatic solution that automates the detection of screen failure data, significantly reducing the workload for data analysts while ensuring the reliability of | adam dataset, sdtm domain, clinical trial, macro parameter, cdisc | source | 5 |
| 134 | Identifying Breaking Changes in R Packages: pkgdiff | David J. Bosak | SAS software is famous for its stability and backward compatibility. SAS programs written in the 1980s will still work today, often with few or no changes. R is a different story. In R, programs you wrote a few months ago and were working perfectly suddenly can have errors or warnings. After some investigation, you realize a package you were using has changed, and broken your program. This is called a "breaking change". The purpose of this paper is to explore an R package named "pkgdiff", which aims to identify and help manage breaking changes. The paper will summarize the major functions of the package, and explain how it can be used to reduce breakages in your code | source | 8 | |
| 135 | Impact of Drug Accountability on Drug Compliance and Dose Intensity in Clinical Trials | Vishal Gandhi, Milan Adesara |
In clinical trials involving oral drug administration with varying dosing frequencies, drug accountability is critical for assessing drug exposure, compliance, and treatment efficacy. Patients are typically dispensed a specific quantity of medication, with any unused tablets returned at subsequent visits. The returned medication is analyzed to determine key parameters, including the Actual Consumed Dose, Actual Dose Intensity and Relative Dose Intensity. These parameters provide a comprehensive understanding of patient adherence to the prescribed regimen, enabling the identification of potential deviations that may impact pharmacokinetic and study outcomes. Ensuring accurate measurement of Drug Compliance directly influences the reliability of study results. Evaluating Actual Dose Intensity and Relative Dose Intensity allows reviewers to compare the administered dose with the intended dose and assess the degr ee to which patients adhere to the treatment plan | dose intensity, clinical trial, adverse events | source | 7 |
| 136 | Implementation of the inverse probability of censoring weighting (IPCW) Model in Oncology Trials | Mei Huang, Shibani Harite, Haijun Ma, Linsong (Athena) Zhang |
Crossover is a common feature in oncology clinical trial designs that allows patient s in the control arm to switch to the experimental arm, often upon disease progression. Crossover can bias the treatment effect estimate for long-term endpoints, such as overall survival. The inverse probability of censoring weighting (IPCW) model is a widely used technique to estimate the treatment effect on overall survival as if no crossover had occurred. Although the implementation of the IPCW model in SAS has been previ ously discussed (Mosier, 2023), applying the IPCW model remains challenging in practice and requires careful consideration. This paper provides a step-by-step guide to implementing the IPCW model, explaining the method in layman's terms and highlighting technical issues and solutions. For instance, there is limited literature on preparing multiple time-varying covariates in a counting process format, which can be cumbersome | time-dependent covariate, overall survival, clinical trial, response variable, treatment effect, feature selection, randomization | source | 17 |
| 137 | Implementing Data Checks for Patient Registries - A Modular Approach | John R. Gerlach, Elizabeth Loisel |
The pharmaceutical industry collects data on patients to evaluate the safety and effectiveness of their products. These patient registries contain copious amounts of longitudinal data on thousands of patients, collected over many years. In order to ensure data integrity, numerous data checks must be performed such that any data issues are reported to Data Management. This paper explains a modular approach to implementing data checks that facilitates extensibility and includes macros whose names have a suffixed macro variable, which greatly decreases the lines of SAS code | data cleaning, macro call, macro variable, %if-%then-%else | source | 8 |
| 138 | Improve Your CRF Review Process: A Python-Based Approach to Capturing CRFs via Browser Automation | Andrew Herndon | Case Report Form (CRF) design and review is a foundational process for a successful clinical trial. It typically occurs following protocol finalization and requires cross -functional feedback from multiple stakeholders such as Clinical Operations, Clinical Development, Pharmacovigilance, Statistics, an d Statistical Programming, often under expedited timelines. However, the default annotated CRFs generated from Electronic Data Capture (EDC) systems are poorly formatted and unreflective of the actual CRF appearance, leading to inefficient reviews that can jeopardize study start-up timelines. We present an automated solution, with complete Python code and implementation steps, that captures CRFs directly from EDC using Python and open-source libraries like Selenium. Our approach authenticates into the EDC system, processes CRF URLs from EXCEL, removes unnecessary UI elements, incorporates dropdown options as text, and generates well-formatted PDFs that accurately represent the EDC forms |
edc, excel, clinical research, clinical trial, cdisc | source | 8 |
| 139 | In-house Data Monitoring Committee Report Programming | Lingjiao Qi, Amanda Plaisted, Sreedhar Bodepudi |
A Data Monitoring Committee (DMC) is an independent group of experts tasked with overseeing the safety and/or efficacy of an ongoing double-blind clinical trial. The DMC periodically reviews accumulated data in unblinded study reports and provides recommendations on the study's conduct. While DMC report activities in pivotal trials are typically external to the sponsor, for non-registrational trials (e.g., Phase 2 trials), it is acceptable for an internal, independent group to manage DMC report activities. This internal approach can streamline processes and enhance efficiencies in both timelines and costs. This paper shares the authors' experience in establishing the first in-house DMC, where an independent group of statisticians and programmers was formed within the organization. It discusses the processes and responsibilities established between the blinded and unblinded programming teams to maintain data integrity and ensure appropriate access for relevant stakeholders | clinical trial, data integrity | source | 7 |
| 140 | Incorporating Frailty into Time-to-Event Analysis: A Practical Approach with R frailtypack | Sunil Kumar Pusarla, Avani Reddy Alla |
Mixed models are commonly used in clinical trials with continuous and binary/ordinal endpoints involving repeated measures data, where both fixed and random effects are used to estimate the treatment effect. However, in oncology and rare disease trials with time-to-event endpoints, random effects are not routinely incorporated into the Cox multivariable regression model . This omission overlooks unobservable covariates that may influence outcomes. Additionally, correlated outcomes are rarely considered in survival analysis. Frailty models extend the Cox model by accounting for unmeasured variables, particularly in clustered or recurrent event data. While SAS allows frailty inclusion in PROC PHREG via the RANDOM statement in a univariate analysis, it cannot handle multivariate joint frailty models. Moreover, it does not provide patient - level predicted frailty scores |
frailty model, likelihood function, proc mixed, clinical trial, hazard function, survival analysis | source | 9 |
| 141 | Incorporating LLMs into SAS Workflows | Samiul Haque, Sundaresh Sankaran |
Python integration with SAS provides developers and convenient way to incorporate LLM frameworks into SAS Studio workflow. Through collaborating with Python developers LLMs can be easily incorporated into different SAS projects. We developed a Retrieval Au gmented Generation (RAG) framework that can be easily incorporated into SAS Studio and leveraged by any SAS Viya user. We showcase how different component of RAG architecture can be implemented within SAS platform by leveraging proprietary and opensource technologies. We discuss how a generic RAG architecture can be used for different use cases such as Adverse Event Detection, Structured Data extraction, Data Mapping, Protocol Search, and Patient Matching. Finally, we provide a blueprint for data scientists to implement their own RAG architecture in SAS Studio for their custom use cases | sas studio, adverse events, proc step, proc http, sas viya, life sciences | source | 7 |
| 142 | Integrated R scripts to Power BI | Jun Yang | Visualization plays a critical role in reviewing, estimating data, and identifying outliers in clinical trials. By combining the capabilities of Power BI and R, researchers can create a user-friendly and efficient platform for developing professional visualization packages with data automation and interactive features. Power BI, developed by Microsoft, is a powerful business analytics tool that enables users to transform raw data into actionable insights through interactive dashboards and reports. On the other hand, R is a widely used programming language in clinical trials, known for its open-source nature, robust statistical capabilities, and extensive community support. Together, these tools offer an effective solution for addressing the data visualization and analytical needs of the pharmaceutical and healthcare industries | waterfall chart, procedures, pd, change from baseline, clinical trial, complete response | source | 6 |
| 143 | Integrating Collaborative Programming with Automated Traceability and Reproducibility in Pharma Studies and Real-World Data Projects by Adapting DevOps Best-Practices | Ariel Asper, Sundeep Bath, Jennifer Dusendang, Yuval Koren, Silvia Orozco |
To enhance integrity of research and study findings, data scientists should ensure that studies are traceable and reproducible, which involves meticulous management of datasets, tracking code changes, and robust storage of results. Without infrastructure to support reproducibility efforts, documentation, dependency management, and version control processes can be manual, unreliable, and unclear. This creates problems with determining when analysis changes occurred, which version of study results were produced by which version of code, and whether all study steps are processed in proper order and appropriately documented. Implementing procedures and technical infrastructure helps to maintain and automate reproducibility and traceability. To ensure that code can be executed consistently across multiple compute environments, we structure analysis scripts into parameterized pipelines within an isolated Docker container environment which specifies all versions and dependencies | snowflake schema, access control | source | 11 |
| 144 | Integrating Generative AI into Medical Writing: Building an Interactive Drafting and Search Framework | Tadashi Matsuno | The increasingly lengthy timelines of clinical development have underscored the growing need to shorten the medical writing process, particularly for key documents such as Clinical Study Reports (CSRs) and protocols. At Shionogi, we have been pursuing a 15 -25% improvement in drafting efficiency through the deployment of an in -house Generative AI solution on a secure cloud platform. This solution leverages organizational templates and historical trial documents along with a Large Language Model (LLM). Nevertheless, due to risks such as so-called "hallucination," it is unrealistic to rely solely on LLMs for producing perfect, submission -ready documents. We have therefore developed a web -based front -end application that enables medical writers to interactively review and refine AI-generated text, focusing on using AI for initial drafts and leaving the finalization to human oversight | clinical research, data science, clinical trial | source | 12 |
| 145 | Integrating SAS DDE to Automate Excel Task Tracking in Pharmaceutical Statistical Programming | Amy Zhang, Huei-Ling Chen |
Statistical programming teams in pharmaceutical companies often use EXCEL spreadsheets to align team members and keep track of the deliverables and programming tasks assigned to programmers and statisticians. Each of these files serves its own purpose, but also shares related information with the others. For instance, the analysis dataset specification file contains spreadsheets with the metadata for all the datasets. Another spreadsheet holds the details for the tables, listings, and figures (TLFs) needed for a deliverable, including TLF titles, output file names, macros, and their calling programs. An additional programming tracking spreadsheet outlines the programming tasks for each SAS program, covering development, validation, and peer review. It is important to ensure that the information in these files remains consistent. Several options exist for creating and populating new spreadsheets, both within SAS and externally |
adam dataset, excel, proc step, dataset, data cleaning, proc import, macro call, proc sort, proc sql, cdisc | source | 8 |
| 146 | Integration Contemplation: Considerations for a Successful ISS/ISE from Planning to Execution | Jennifer McGrogan, Mario Widel |
During the clinical development of an investigational drug, there are many potential reasons for ceasing advancement of the product. However, when the drug is successful, part of the submission process for approval by regulatory agencies, like the FDA, will require integrated summaries of safety and efficacy (ISS/ISE). Even if every study participating in the integration have achieved their goals to perfection, they are ultimately not designed for the purpose of integration, and the pooling will present cha llenges. Thus, for proactive, and perhaps optimistic, study planning based on the assumption for the drug to be successful, it would be prudent to plan ahead for an ISS/ISE submission. Programmers tasked with integration of various studies often find that they are not immediately suitable for integration, which presents various technical challenges | adam dataset, sdtm domain, ctcae grade, cdisc | source | 8 |
| 147 | Interactive Longitudinal Data Analysis and Visualization in Clinical Research Using R. | María José Gómez Ramírez | Analyzing a longitudinal study with clinical data allows us to understand changes that have occurred over time in patient outcomes (such as biomarkers, the effects of treatments, and disease progression, among others). Graphical visualization of the data is crucial, as it enables decision-makers to interpret analytics in a visual format, making it easier to grasp complex concepts while identifying new patterns. Moreover, when you can interact dynamically with the application presenting the data, they can de epen their understanding of an event or uncover specific insights. The use of R and its different free available packages makes it possible to create user -friendly data visualization applications that allow for diverse interactive forms. This paper will demonstrate an example of an application using R packages such as "shiny", "lme4", "plotly", "DT" and others, to present an interactive visualization of a longitudinal data analysis using the Admiral's publicly available dataset | machine learning, data science, change from baseline, box plot | source | 8 |
| 148 | Key guidelines, Tricks and Experiences for PMDA and comparison with FDA and CDE submission | Ramesh Potluri | Submitting documents to regulatory authorities such as the Pharmaceuticals and Medical Devices Agency (PMDA) and the Food and Drug Administration (FDA) is a complex task requiring careful preparation and an in-depth understanding of regulatory requirements. This paper outlines key guidelines and highlights the differences between submissions to PMDA, FDA, and the Center for Drug Evaluation (CDE). Additionally, it provides practical tips and shared experiences to equip programmers and regulatory teams with the necessary knowledge for efficient PMDA submission proces ses and effective handling of regulatory inquiries | data cleaning, adam dataset, adverse events, data validation rules, clinical trial, cdisc | source | 6 |
| 149 | Landmark Analysis: A Method for Accurate Prediction of Time-Dependent Clinical Risks and Their Effect on Patient Outcomes | Sunil Kumar Pusarla, Avani Reddy Alla |
Estimating the effects of time-dependent variables on patient outcomes is challenging due to their changing values over time. Landmark analysis offers a practical solution by assessing clinical outcomes at predefined time points, transforming time-dependent variables into fixed covariates for clearer interpretation. The Cox model with time-dependent variables approximates a dynamic landmark analysis with infinitely many time points. If the parameter for the landmark time point is zero, both methods may yield similar results. However, unlike standard Cox regression, which can introduce immortal time bias when analyzing post-baseline variables, landmark analysis ensures fair comparisons by evaluating all patients at the same time points. Landmark analysis can be performed using SAS PHREG procedure. By examining multiple landmark time points, landmark analysis enables graphical visualization of time dependent covariate effects using SAS SGPANEL, providing clinically relevant insights |
time-dependent covariate, proc step, response variable, proc phreg, cause-specific hazard, survival analysis | source | 8 |
| 150 | Last Observation Carried Forward (LOCF) in Longitudinal Patient Studies: A Functional Approach to Imputing Missing Values Using PROC FCMP, the SAS Function Compiler | Troy Martin Hughes | Last observation carried forward (LOCF) is a ubiquitous method of imputing missing values in longitudinal studies, and is commonly implemented when a subject (i.e., a patient or participant) misses a scheduled visit, and data cannot be collected (or generated). In general, the last "valid" value from a previous visit is retained for the later visit on which the data could not be obtained, and this conservative estimation succeeds in cases where the actual value would have been little changed. Nuanced criteria may stipulate which prior values count as "valid" (e.g., after the start of treatment) as well as for how long (e.g., how many days, visit weeks, consecutive missed visits) a value can be used to impute other values . Given these complexities, LOCF solutions implemented in SAS historically adopt a procedural approach, and often require multiple DATA steps and/or procedures to impute data both across observations and within subjects | imputation, proc fcmp, missing value, data validation rules, last observation carried forward, retain | source | 9 |
| 151 | Leading Through Change: Motivating Programming Teams During Mergers and Integrations | Shefalica Chand | When a larger pharmaceutical company acquires a smaller one, the integration process often brings challenges for the acquired company's programming leadership. During this transition, it is crucial that the smaller company's leaders maintain motivation, morale, and productivity among their teams. This paper discusses strategies to keep teams engaged, focused on critical projects, and aligned with the new organizational goals. A key strategy is transparent and empathetic communication. Leaders should address concerns, provide clarity on future direction, and help employees understand their role in the broader context of the merger. Building trust is paramount; leaders should consistently acknowledge the contributions of team members and reassure them about the value they bring to the new organization. Encouraging a sense of continuity is also essential. By emphasizing shared goals and outcomes, leaders can help employees see how their efforts directly contribute to the larger mission | clinical research, job security | source | 11 |
| 152 | Leveraging Gen AI (ChatGPT, Gemini) API for Advanced Biometric Analysis in SAS, Python and R | Kevin Lee | Since its release, ChatGPT has rapidly gained popularity, reaching 100 million users within 2 months. Not only we could use ChatGPT for prompting, but we could build application / codes using API. The paper explores the integration of Gen AI (e.g., OpenAI, Gemini) API into biometric data analysis workflows using SAS, Python, and R programming languages. The paper will display the workflow of Gen AI integration and provide sample codes for SAS, Python, and R programming. First, the paper will show how to create API in OpenAI (paid) and Gemini (free version). Secondly, it will help to create the input data containing prompts and the questions to Gen AI. Thirdly, it will create codes (e.g., SAS, Python, R) to read the input data and to generate output data. Fourth, it will show how to read output data to obtain the results from ChatGPT. The paper also presents a document-based question-answering system leveraging LangChain, Retrieval-Augmented Generation (RAG), and OpenAI models | data visualization, data cleaning | source | 10 |
| 153 | Leveraging Health Technology Assessment (HTA) for Market Access: A Statistical Programming Perspective on German HTA Submission | Rachana Agarwal, Wendy Wang |
Health Technology Assessment (HTA) in European countries is an essential process that goes beyond regulatory approval by assessing a drug's real-world value. While regulatory submissions focus on proving safety and efficacy, HTA evaluates additional factors to guide pricing, patient access, and reimbursement decisions. In Germany, the Gemeinsamer Bundesausschuss (G-BA) leads the HTA process, using clinical evidence-including Patient-reported outcomes (PRO), such as quality of life (QoL), safety, and efficacy-to establish a drug's value proposition. Demonstrating significant benefits in these areas can result in favorable pricing and improved revenue outcomes, making HTA a key determinant of successful market access. To ensure patient access to innovative therapies, securing broad reimbursement coverage for specific patient subgroups is essential | adverse events, regulatory submission, subgroup analysis, adam dataset, clinical trial | source | 5 |
| 154 | Leveraging previous study data for Extension studies: Structuring Subject and Participant Level Analysis datasets using CRF data | Priyanka Thumuganti | The main purpose for conducting Long-Term Extension (LTE) studies is to analyze the long-term impact of an investigational drug on subjects, thereby validating or reevaluating the findings of the Parent study. Linking subject-level data between LTE study and Parent study is crucial for maintaining continuity and coherence in longitudinal research. This paper explains how subject-level data from LTE study can be integrated with Parent study data to strengthen the evidence required for regulatory approval | clinical trial, subject-level data, treatment effect, adam dataset, cdisc | source | 7 |
| 155 | Leveraging Python for Statistical Analysis in Public Health: Techniques and Visualizations for Life Sciences Professionals | Michael Carnival | Public health is of vital importance to society, aiming to protect and improve the health of individuals and communities through disease prevention, health promotion, and equitable access to healthcare services. A robust public health system not only fosters healthy lifestyles but also ensures the availability of skilled healthcare professionals, maintains high-quality standards, and addresses health disparities. To achieve these goals, statistical methods and analytical techniques are essential for identifying trends, address ing challenges, and evaluating the effectiveness of public health interventions. This work focuses on leveraging Python for statistical analysis using real-world public health data. Key topics include generating visualizations such as boxplots, histograms, scatterplots, and pie charts, as well as performing statistical analyses like correlation coefficient computation, normality testing, ANOVA, and goodness -of-fit tests | correlation, histogram, box plot, goodness of fit, data science, anova, normal distribution, proc freq, big data, life sciences, proc means, sample, nominal variable, interval variable, one-way anova, outlier, qq plot | source | 14 |
| 156 | Listing Shell To Sas Program Automation Tool | Balaji Ayyappan, Johnny Tai |
Listings in clinical reports are required part of the documentation for clinical trials and used for statistical review by regulatory authority. We created a tool that can automatically generate the SAS programs to produce multiple listings, when we pass the TFL shell document (Table, Figures and Listings shells). Tool can identify the listing shell pages from shell and uses the shell annotation information, metadata and value level information from the datasets while generating the SAS code. SAS and VBA applications are used to develop this tool. Based on the annotated variables, for example: analysis flag used in title - we can subset required subjects from analysis dataset, character or numeric date variables are converted to company specified character date format in reporting, columns width in PROC REPORT proce dure and page number variable to determine number of lines to be printed in each page are automatically determined |
proc report, proc step, output delivery system | source | 6 |
| 157 | Low-Code Solutioning in SAS Viya for Automated Clinical Data Quality, Decisioning and Harmonization | Mary Dolegowski, Scott McClain |
Fact checking clinical data quality is a ubiquitous need in drug trials. It's typically a very manual, code- heavy, time-consuming process. Complications increase due to multiple data vendors . The pharma customer demand is for a reduction in human error and to speed up data processing. Our SAS objective was to automate quality review using statistical models, language models and business rules to reduce inaccuracies and time. A full application interface was created to allow human review at critical points in decision making and data harmonization. The product result is an automated end-to-end, low-code pipeline that reduces human-in-the-loop manual review with Viya out-of-box capabilities, supporting better time-to-registration for drug development | score code, machine learning, champion model, sas viya | source | 10 |
| 158 | Macro to Compare ADaM Spec Derivations Against Available SDTM Data | Ingrid Shu, Kexin Guan, Jeff Xia |
An ADaM specifications file establishes the groundwork for developing ADaM datasets while maintaining CDISC standards. The specifications are comprehensive, and of these, the ADaM variable derivation rules are particularly critical and susceptible to errors. Ensuring traceability between the source data (SDTM) and the analysis data is a core principle of ADaM specifications. Oftentimes, the programmer will use a global standard ADaM spec and adjust the derivation rules to fit their specific study needs. This process can be time-intensive and requires extensive cross-checking between specification derivations and the actual available values of raw SDTM data. This SAS macro offers a solution by detecting most, if not all, cases where the specification derivations are incongruent with the existing SDTM data. It systematically reviews the entire ADaM specification and identifies value mismatches by using regular expressions | adam dataset, excel, sdtm domain, raw data, cdisc | source | 9 |
| 159 | Mastering Modern Leadership Through Authenticity, Empathy, Purpose, and Influence | Jyoti (Jo) Agarwal | Leadership is not defined by a title. It is about influence, impact, and the ability to inspire others. This paper explores the essential traits, styles, behaviors, responsibilities, and priorities that shape effective leadership in today's complex and rapidly evolving world. By focusing on authenticity, empathy, and purpose, and drawing on leadership models such as Authentic Leadership and Empathy & Influence, it examines how these qualities enable leaders to inspire, motivate, and connect with others. Drawing on established leadership models from Bill George and Simon Sinek, this paper delves into the importance of leading with a clear sense of purpose and cultivating genuine relationships with colleagues. Leaders who embrace authenticity and empathy foster a culture of trust, loyalty, and collaboration, enhancing both organizational performance and employee satisfaction | source | 7 | |
| 160 | Mastering the Maze of Oncology Endpoints: A Unified SAS Approach for Randomized Controlled Trial Analysis | Yuxin Wang, Kelly Chao, Wenqing Yu, Hongbing Jin |
In oncology clinical trials, particularly in randomized controlled studies, the analysis of diverse endpoints is crucial for evaluating treatment efficacy compared to the control arm. This paper presents a comprehensive, unified approach to analyzing three fundamental types of endpoints in oncology research: time-to-event, categorical and continuous. By consolidating these methodologies into a single, accessible resource, we aim to provide an invaluable reference for statisticians, programmers, and researchers in the field. Our work focuses on three key areas of statistical analysis, each tailored to a specific endpoint type. Time-to-Event Endpoints: We examine both non-parametric (Kaplan-Meier) and semi-parametric (Cox proportional hazards regression) methods for analyzing endpoints like overall survival (OS) and progression-free survival (PFS). The paper introduces PROC LIFETEST for survival curve generation and estimation and PROC PHREG for Cox regression and model evaluation |
proc step, dataset, sample, chi-square test, clinical trial, sample size, confidence interval, hazard ratio, proc freq, proc mixed, imputation, exact test, multiple imputation, treatment effect, missing value, proc mi, fisher exact test, objective response rate, time-to-event endpoint, correlation, covariance, proc phreg, log-rank test, overall survival, rank test, survival analysis, censoring, covariance structure, median survival, progression-free survival, regression analysis, proc means, data science | source | 11 |
| 161 | Methodology for AI-driven Outcome Prediction for Patients with Atrial Fibrillation After Transcatheter Aortic Valve Implantation (TAVI) | Felix Just, Krishna Padmanabhan, Parth Jinger, Amanda Borrow, Rüdiger Smolnik, Eva-Maria Fronk, Neelam Yadav, Sergei Krivtcov, Stas Zakharkin, Martin Unverdorben, Asmir Vodencarevic |
The identification of risk factors for adverse clinical outcomes using traditional statistical methods ( e.g., Cox regression) has provided physicians with valuable insights for treatment decision -making. However, these methods are often restricted to capturing only linear associations within the data. Artificial Intelligence (AI) and Machine Learning (ML) overcome this limitation, offering the potential to uncover complex, non-linear relationships, thereby providing powerful new insights at the patient level. We introduce an ML approach to develop predictive models for four clinical key endpoints from a randomized controlled trial: ischemic stroke, major gastrointestinal bleeding, major or non-major clinically relevant bleeding, and net adverse clinical events. Our methodology involves a pipeline where 1 0 ML algorithms are trained, optimized, and evaluated using nested cross-validation | logistic regression, f1 score, shap, logistic distribution, regression analysis, hyperparameter tuning, machine learning, data cleaning, imputation, adverse events, clinical endpoint, feature selection, randomized controlled trial, nominal variable, clinical trial, test set, missing value, training set | source | 6 |
| 162 | Middle Manager-s Playbook: How to Build and Lead a Strong Remote Team | Yuka Tanaka-Chambers | In a global company, building a strong team in a remote environment presents both unique opportunities and challenges. Remote work allows access to a wider pool of global talent, and employees can benefit from working in the safe and comfortable environment of their homes. However, motivating employees, identifying disengagement, fostering genuine connections, and cultivating a sense of belonging are among the key challenges leaders face in remote settings. Without the traditional face- to-face interactions of a shared workspace, these efforts require intentional strategies, strong communication, and a deep understanding of team dynamics. In this presentation, I will share practical solutions, including the effective use of tools like Microsoft Teams, hosting engaging virtual events, encouraging meaningful small talk, and adopting key leadership practices | source | 9 | |
| 163 | Mining Data from PDF Files Using SAS | Michael Stout, Brian Knepple |
SAS is a powerful software tool and can read and process data from multiple file formats. SAS can efficiently read non-SAS datasets, such as Text, EXCEL, and CSV files. It is much more challenging to read and process files in PDF (portable document format) format. This paper describes an approach and pitfalls of mining data embedded in PDF files. Reading and processing data from PDF files can be challenging, but the gain is worth the pain. This process has reduced the amount of time and improved accuracy of manually collating data for summary reports |
excel, input statement | source | 5 |
| 164 | Multiple Imputation Techniques in the Context of ADaM Datasets | Shunbing Zhao, Linping Li |
Multiple Imputation (MI) is an effective and increas ingly popular method for handling missing data in regulatory clinical studies. Using MI method, each missing value is replaced with a set of plaus ible values. It bec omes challenging to build a CDISC-c ompliant ADaM dataset from this multiply imputed dataset. In this paper, from a s tatistical programmer's perspective, we use two detailed examples to illustrate how to implement the multiple imputation techniques in the generation of CDISC-compliant ADaM datasets. In the first example, we elaborate on how to build an ADaM Basic Data Structure (BDS) dataset to perform a MI sensitivity analysis using a control-b ased pattern mixture model. In this example, we show how to use SAS MI procedure to generate an analysis dataset and how to transform it into ADaM standardized dataset step by s tep. In the second example, we demons trate how to build a time-t o-event BDS dataset using a simulation-based jump-to-reference MI approach |
adam dataset, proc mi, missing value, proc step, imputation, multiple imputation, sensitivity analysis, sample, clinical trial, regression analysis, data science, cdisc | source | 11 |
| 165 | Name That ADaM Dataset Structure | Nancy Brucken | This presentation will give a brief overview of the standard ADaM dataset classes, followed by an interactive segment where a commonly-used table shell will be displayed, and the audience will be encouraged to name the standard ADaM dataset class and variables that could be used to most easily generate that table. Alternative approaches will be discussed where appropriate, includ ing the use of the ADaM Other class and when it is acceptable to produce a single table from multiple ADaM datasets. The goal is to get the audience thinking about designing ADaM datasets based on table requirements, instead of on the structure of the SDTM domains feeding into those datasets | adam dataset, adverse events, change from baseline, vital signs, proc means, population pk, compartmental analysis, key, data science, cdisc | source | 8 |
| 166 | Navigating Compliance Excellence: ISO Standards & Data Privacy Implementation | Ashwini Kanade, Syamala Schoemperlen |
Organizations face challenges aligning operational practices with global quality, security, and privacy standards in today's data-driven world. This paper explores the foundational principles and implementation strategies for ISO standards, including ISO 9001 (Quality Management), ISO 27001 (Information Security), ISO 22303 (Business Continuity), ISO 27701 (Privacy Information Management), and the General Data Protection Regulation (GDPR) and other global privacy regulations. The focus is on our approach to integrating these standards into its operational framework, emphasizing practical methods for ensuring compliance while fostering efficiency and resilience. Topics include structured management systems, risk assessment frameworks, data governance policies, and stakeholder engagement. We also offer insights into the implementation process, highlighting best practices, challenges, and solutions tailored to a global, cross-functional environment | privacy, data protection, access control | source | 15 |
| 167 | Navigating the transition of legacy processes for SDTM creation | Jyothi Ketavarapu | Transitioning from legacy systems and processes into clinical trial data management is a critical yet complex undertaking. With industry leaning towards open -source technologies, product -based solutions, and targeted automation, organizations face the chal lenge of selecting or building scalable and compliant systems. This paper explores key considerations for transforming Electronic Data Capture (EDC) data into Submission Data Tabulation Model (SDTM) datasets, emphasizing the importance of balancing innovation with operational efficiency and regulatory compliance. A successful transition begins with a thorough understanding of the end -to-end process, from data collection to SDTM creation. Pain points, such as delays caused by a lack of standardization, redundant steps, or disconnected tools, must be addressed. Solut ions range from point automations to complete system overhauls, with a focus on ensuring interoperability, data governance, and quality | data governance, clinical trial, clinical data, 21 cfr part 11, data integrity, regulatory submission, data cleaning, outlier, cdisc | source | 5 |
| 168 | Optimized Resourcing Strategies in Statistical Programming within CROs and Considerations for AI Integration in Resource Management | Vihar Patel | In the dynamic environment of Contract Research Organizations (CROs), an optimized resourcing strategy is crucial for efficient and successful statistical analysis in clinical trials. The pharmaceutical industry is rapidly evolving, with accelerated drug development processes and overlapping submissions necessitating innovative approaches to manage timelines, quality, resources, and risks. Effective resource management is crucial for balancing the capacities, personal growth, and business needs of statistical programming teams while ensuring successful deliverables. This paper explores holistic approaches to optimized resourcing, emphasizing strategies, tools, and AI considerations essential for enhancing the effectiveness of statistical programming teams. Key areas of focus include balancing business demands with personal growth, determining priorities, skillset-based resourcing, mentoring, cross-regional collaboration, and staff turnover maintenance | clinical programming, resource allocation, pharmaceutical industry, clinical research, statistical programmer | source | 10 |
| 169 | Optimizing SAS Programming Pipelines Using the %Unpack and %SearchReplace Macros for Version Control and Customization | Ning Ning, Assir Abushouk |
This paper introduces a two-part macro solution designed to streamline SAS programming workflows, specifically targeting version control, customization, and pipeline optimization in environments such as pharmaceutical companies and contract research organizations (CROs). The first part of the solution, the %Unpack macro, extracts files from a zip directory, enabling quick access to the latest versions of SAS macros, programming shells, and directory structures. The second part, the %SearchReplace macro, automates the process of searching for and replacing specific strings within SAS programs in specific folders to customize them according to user needs. We demonstrate the benefits of this integrated solution for regulatory compliance and pipeline efficiency and explore the potential for using each part of the macro independently for various tasks across different environments | sas programming, clinical programming, data science, macro definition, sample, macro variable | source | 8 |
| 170 | Our Second Brain: A Guide to Building Note System for SAS Programmer in Clinical Trial | Zeyu Li | As a clinical trial SAS programmer, the job may at first seem overwhelming with innumerable details necessary to apply programs and analyses. Added to this, months later you could be asked by statisticians or data managers to comment on issues within the data of previous studies. With numerous studies to handle each day, remembering every piece of information from every program and project is not feasible. For that reason, there is a strong need for having an explicit and well-structured note system. A well-designed note system is your own personal knowledge base, from which you can recall essential information about studies instantly, sometimes even years after they were completed. It also makes you more efficient by providing you with immediate access to reference codes, SAS shortcuts, and reusable templates, thereby avoiding having to do things twice | clinical trial, sas programmer, data science, clinical programming | source | 5 |
| 171 | Packing PDF TFL with Table of Contents and Bookmarks Using Python | Jun Yang, Yan Moore |
Packaging tables, listings, and figures (TLFs) is a common requirement in the pharmaceutical industry. Rich Text Format (RTF) is widely used for generating individual reports, as SAS offers powerful and flexible functions for RTF output. Consequently, RTF is often preferred for combining outputs, and numerous standard programs and macros have been developed for this purpose. However, RTF has certain limitations. Its display may vary across different operating systems and even between versions of Microsoft Word. Additionally, RTF files can be thousands of pages long, leading to large file sizes that are slow to open, browse, and transfer. Portable Document Format (PDF) addresses these challenges. A converted and merged PDF file offers a smaller, more consistent, and manageable solution. This paper introduces an alternative approach to converting RTF outputs into a single, well-structured PDF file. It has two options to generate a combined pdf with TOC | dataset, error message | source | 14 |
| 172 | PDFs Done Right: The Statistical Programmer-s Guide to Flawless Regulatory Submissions | Srivathsa Ravikiran, Sri Raghunadh Kakani |
In the pharmaceutical industry, regulatory submissions require meticulous preparation to ensure compliance with stringent guidelines. A key component of this process involves preparing Case Report Tabulation (CRT) packages, which include critical PDF documents such as annotated case repo rt forms (aCRF), define.pdf, the study data reviewer's guide (sdrg.pdf), and the analysis data reviewer's guide (adrg.pdf). These documents play a pivotal role in Module -5 submissions, where consistency, quality, and technical accuracy are imperative for smooth health authority reviews. This paper outlines a standardized framework for optimizing PDF deliverables used in CRT packages. Specifically, we focus on enhancing the quality and compliance of the PDFs generated within programming for submission. Our proposed approach integrates the use of the Lorenz eValidator to evaluate and validate PDFs according to the technical attributes mandated by FDA guidelines and other regulatory agencies | data cleaning, statistical programmer, regulatory submission, clinical programming, cdisc | source | 12 |
| 173 | Practical considerations for Intercurrent Events and Multiple Imputation | David Bushnell | Estimands in clinical trials provide the framework to estimate the treatment effect given the influence of intercurrent events (ICE). ICE occur after randomization or initial treatment and are defined in terms of how they affect study assessments or the interpretation of final results. However, ICE are not merely incidental missing data, they are defined by their relation to study treatment and the clinical question posed by the trial. When the estimand and ICE are conceived, it is important to have both clinical and statistical input; ICE strategies other than 'treatment policy' should be justified. The strategy for addressing ICE must properly evaluate how events could be related to treatment evaluation. Practical consideration must be made for handling ICE along with other/incidental missing data. Multiple imputation process must be consistent with the ICE strategy | missing value, intercurrent event, imputation, while-on-treatment, multiple imputation, proc mi, clinical trial, repeated measures, sensitivity analysis, proc step | source | 6 |
| 174 | Practical Process in SAS of Using External Controls | Hui Mao, Na Wang, Lixin Gao |
Usage of real-world data (RWD) or historical data as external controls (EC) for assessing treatment effect in rare disease or early oncology drug development are increasingly accepted by regulatory agencies. Various methods have been developed to construct external control arm for single-arm trial or supplement control arm in the current trial. In this paper, we present practical implementations in SAS for the process to generate well-matched EC along with analytic methods in different settings, as well as relevant SAS codes and summary outputs | propensity score, treatment effect, baseline characteristics, logistic regression, response variable, logistic distribution, regression analysis, predictor variable | source | 5 |
| 175 | Proc LIFE-REFLECT: Surviving the Leadership Journey | Steve Nicholas | The journey from intern to leader in the life sciences industry is rarely a straight path - it's full of challenges, unexpected obstacles, and invaluable lessons. Success in leadership requires more than just technical expertise; it demands an understanding of people, culture, and connection. This paper explores critical skills for effective leadership, from fostering trust and accountability, to creating a culture of collaboration and engagement. Great leaders don't just lead - they mentor. Leadership is a balance of strategic vision and personal connection, creating high-performing teams while prioritizing continuous growth. By investing in relationships, developing communication skills, and embracing mentorship, leaders empower both themselves and those around them. Reflecting on my own journey, I share the skills and mindset shifts that helped me transition from an individual contributor to a leader | life sciences, clinical trial | source | 7 |
| 176 | Productive Safety Signal Detection and Analysis using In-context Learning | Sundaresh Sankaran, Samiul Haque, Sherrine Eid |
Analysis of Adverse Event (AE) narratives enables better patient outcomes by providing rich safety signals. In addition to time-consuming manual review, life sciences organisations and regulators have used Natural Language Processing (NLP) to classify and extract signals. However, NLP's value can be constrained due to its complex nature, interpretability, and difficulty to scale. In this session, we demonstrate how NLP, combined with In-context Learning, a Generative AI technique, can efficiently structure AE narratives into easily interpretable datasets. In-context learning helps us specify an instruction template and provide context to Large Language Models (LLMs), using a customisable checklist containing numerous dimensions on which safety reviewers analyse AEs. Checklist responses are assessed and consolidated into scorecards, which are then surfaced through SAS reports and visualisations along with insights and patterns | adverse events, sas studio, correlation, life sciences | source | 11 |
| 177 | Programmatic Annotation of Case Report Forms | Matthew Finnemeyer | Annotation of Case Report Forms (CRFs) is an essential yet time-consuming task; manual creation or import of annotations as Comments to a PDF can be tedious and error prone. This paper outlines a robust macro that: imports pre-existing PDF annotations into SAS, parses key information for user review and modification, "up-versions" annotations to conform with the latest standards, aligns annotations with their "target page" in the Destination CRF, and exports the finalized annotations to a Forms Data Format (.FDF) file for direct import by the user. This macro has reduced internal programmer -hours for CRF annotation by more than 50% when creating new or modified aCRFs | dataset, file statement | source | 9 |
| 178 | Programming Challenges in Master Protocols | Vijaya Jonnalagadda | Master protocols are becoming increasingly vital in clinical trials, as they facilitate the simultaneous evaluation of multiple treatments or hypotheses within a single, unified trial framework. However, the adoption of master protocols presents unique programming challenges that must be carefully considered by management teams. In clinical trials, master protocols allow for the concurrent testing of several treatments or strategies, optimizing resource use and enabling real-time adjustments based on interim analyses. This paper explores the statistical programming challenges that arise during the design, execution, and analysis phases of such trials, including the development of dynamic programming and the integration of adaptive designs | clinical trial, statistical programmer, treatment arm, adverse events | source | 6 |
| 179 | Programming Perspectives for Efficient and Accurate C-QTc Analysis | Lingjie Zhang, Lata Maganti, Richard Moreton, Runcheng Li |
The significance of Phase I concentration-QTc (C-QTc) analysis in clinical trials lies in its ability to assess potential drug-induced cardiac effects, critical for ensuring patient safety. The paper presents details on the integration of modeling techniques combined with the systematic organization of data via ADaM datasets and statistical analysis that significantly enhances the reliability of C-QTc findings. The paper also presents challenges and solutions integral to programming associated with the preparation of the analysis data and C-QTc analysis | proc mixed, proc step, clinical trial, geomean, data science, regression analysis, change from baseline, confidence interval, adam dataset, response variable, cdisc | source | 11 |
| 180 | Promising Zone Designs for Sample Size Re-estimation in Clinical Trials: Graphical Approaches Using SAS | Zhao Yang, Shivani Nanda |
The promising zone design is a highly valuable tool for achieving more efficient and effective drug de- velopment. It allows for modifications of sample size based on unblinded interim data, enhancing trial efficiency and the likelihood of success. During the design stage of a trial, clear communication among various functional stakeholders is crucial to understand and align on the proposed promising zone design. Graphical displays can effectively facilitate this communication process. However, there are currently no resources in SAS to readily create these desired graphical displays. This paper presents SAS programs for producing these graphical displays and includes brief yet informative key underlying technical details. An example is provided to demonstrate its implementation. Hopefully, these accessible SAS programs will encourage broader application of promising zone designs, which are expected to play an increasingly important role in developing new therapies | sample size, conditional power, sample, significance level, type i error, clinical trial, treatment effect, data visualization, final analysis, sample size re-estimation, macro definition, interim analysis, clinical research, proc sgplot, phase iii, stage 1, proc step | source | 18 |
| 181 | Putting the 'R- in RWD: Leveraging R and Posit to enhance Real World Data Programming | Darren Jeng, Sachin Heerah |
The Pfizer Real World Data (RWD) programming team has leveraged R and Posit services to enhance the capabilities of its programmers. We have designed an R package, Shiny apps and even a Quarto website to support all programmers with varying backgrounds, including those with only SAS experience. Our R package is designed to simplify database queries, utilize both R and SAS variable syntax, and standardize deliverables. We have leveraged Posit's RStudio features such as code snippets to make code templates readily accessible for all users within the IDE. Code guides are also presented as snippets to allow all users to load example data and explore standard RWD programming workflows. Overall, embracing and leveraging the features available to us in R and Posit is enhancing our workflow through integrated resources and documentation | proc sql, macro definition, sample | source | 7 |
| 182 | Python with Hermione: Unleash Your Inner Coding Witch & Dragon | Charu Shankar, Jim Box |
Join Hermione Granger on a thrilling 60-minute coding journey where you'll learn to slay coding challenges like a true wizard-using Python as your magic wand! Just as Hermione faces down dragons and dark spells with wit and determination, you'll tackle the essentials of Python programming with her strategic approach to problem-solving. In this fun and fast-paced class, you'll learn to master Python's core elements: variables, functions, and loops-all with the same precision Hermione uses when crafting complex spells. Python's readable syntax will make you feel like you're casting spells in no time, whether you're automating tasks or working with data. By the end of this adventure, you'll be equipped to conquer coding challenges like a seasoned wizard, ready to take on any dragon (or program) that comes your way. Grab your wand (keyboard) and let's start coding like Hermione! | proc python, dataset, proc step, data science | source | 20 |
| 183 | R Programming in SAS LSAF: How to Generate Clinical Reports Using an R Session | Praneeth Adidela | The SAS Life Science Analytics Framework (LSAF) provides an integrated system for transforming, analyzing, reporting, and reviewing clinical research data. LSAF is a cloud-native, single solution for clinical analysis and submission and offers integrated features for clinical data repository, regulatory compliance, quality control, job manifests, version control, audit trails, e -signatures, and documentation support. It enables automation through workflows, supports integrations, and ensures the proper implementation and management of data standards and controlled terminology. LSAF version 5.3 and later introduces integration with R. The program editor within LSAF allows users to code in SAS or R on the same flexible, open platform. Users can develop R programs in LSAF within the R session using an interface similar to the SAS session. The repository and w orkspace store R program files (.r), data files (.rdata or .rds), and log files ( .rlog) like their SAS counterparts | clinical data, clinical research, dataset, demographics table, confidence interval, adverse events, life sciences | source | 12 |
| 184 | Reach for R Low Hanging Fruit for Faster Results | Sunil Kumar Gupta | With the pharma industry expanding packages within pharmaverse, is your organization ready to reach for R low hanging fruit for faster results? This presentation will show how R packages and functions are 'out of the box' production ready for fast results without having to invest in SAS silo resources or expenses. While some R mentoring is required to understand key differences with SAS and R fundamental concepts, smarter organizations can get a jump start on their submission ready deliverables. R packages in data management and graphs enable 'plug-n-play' queries and plots. The R packages used to create the Clinical Study Report (CSR) tables and lists offer R script templates that can be customized. CDISC submission support packages include the define.xml and tidyCDISC. Finally, popular pharmaverse packages rtables, admiral and teal will also be featured | clinical trial, csr, pharmaceutical industry, cdisc | source | 4 |
| 185 | Rising Above Artificial Intelligence: Feeling Confident With Your Human Intelligence | Priscilla Gathoni | In a rapidly advancing world dominated by technological innovation, AI is often seen as a threat to human intelligence and job security. However, I argue that rather than replacing human capacity, AI can complement and elevate human skills. By embracing AI, humans can focus on what truly sets them apart-creativity, emotional intelligence, and the ability to think critically. Despite AI's impressive capabilities, the uniqueness of human thought cannot be replicated. I propose the value of investing in our innate creative energy and the concept of "cosmic data"-the wealth of unique human experience and intuition AI cannot replicate. Through reskilling, rebranding, and overcoming the fear of AI, I offer strategies for individuals to adapt and thrive alongside technological advancements. By reimagining the role of humans in the workforce and embracing AI, we can ensure a harmonious and sustainable future where both humans and machines coexist, complementing each other's strengths | artificial intelligence, excel, data science | source | 15 |
| 186 | Risk Mitigation Solution for Kaplan-Meier Plot in Drug Labeling | Bingjun Wang, Jane X. Liao, Suhas R. Sanjee, Jeff Xia |
Efficacy results are often included in the drug label, and statistical programmers are required to deliver related figures to support this. The stakeholders in regulatory affairs typically modify the annotations on the figures manually to translate into other languages for worldwide regulatory submissions. This non- programmatic process is challenging and error prone. It is important to allow modifications only for certain annotations and ensure that statistical values and graphic elements are non-editable. In this paper, we propose methods for creating Kaplan-Meier (K-M) plots, which are commonly included in the oncology studies for drug labeling. We describe two methods to fulfill these requirements. The first approach involves utilizing SAS and VBA tools. Second approach uses R Shiny application. Both approaches assure accuracy, consistency, and reproducibility | macro definition, survival analysis, clinical trial, macro call, regulatory submission, statistical programmer, kaplan-meier plot | source | 13 |
| 187 | Roadmap to Efficacy Analysis for Early Phase Oncology studies | Dhruv Bansal, Christiana Hawn, Chris Kelly |
Oncology studies are among the most complex and challenging therapeutic areas for statistical programmers. Navigating the efficacy analysis for these studies can be particularly daunting for those new to the field. This paper aims to provide a comprehensive overview of the most common efficacy outputs requested for early phase oncology trials, along with basic codes and techniques to assist in programming these outputs. The paper will discuss common efficacy outputs generated for solid tumor studies, which are the most prevalent type of oncology trials. Solid tumor studies typically follow RECIST (Response Evaluation Criteria in Solid Tumor) guidelines. This paper will focus on the roadmap to efficacy analysis, including the creation of efficacy datasets (ADTR, ADRS, ADTTE) and the parameters used to create outputs for duration of response, Kaplan-Meier estimates for survival analysis, waterfall plots, spider plots, and swimmer plots | best response, efficacy summary, percent change from baseline, swimmer plot, tumor identification, waterfall chart, sample, change from baseline, duration of response, time to response, randomization, progression-free survival, pd, adam dataset, baseline, sdtm domain, standard deviation, time-to-event endpoint, tumor results, cdisc | source | 23 |
| 188 | Route Sixty-Six to SAS Programming, or 63 (+3) Syntax Snippets for a Table Look-up Task, or How to Learn SAS by Solving Only One Exercise! | Bartosz Jabłoński, Quentin McMullen |
It is said that a good programmer should be lazy. And what about a good programming teacher? We dare to say the same is true. This article will show that you can be lazy and also be able to teach an entire SAS○Rcourse at the same time! The aim of the article is to present a variety of examples of how to do one of the most common data processing programming tasks, table look-up, in SAS. We don't assess these methods from a benchmarking or performance perspective but rather present them as an intellectual puzzle. Our goal is to explore how much SAS syntax (statements, PROCs, functions, etc.) could be taught using only one exercise. If you are a fan of unorthodox SAS programming, curious to learn about the variety and flexibility of the SAS language, or an innovative SAS teacher - this article is for you! Table of contents | proc step, macro variable, proc sql, big data, proc transpose, create table, macro loop, proc sort, cas action, if-then-else, proc fcmp, dataset, hash table, merge statement, proc append, set statement, where statement, by group processing, index, txt, proc format, proc iml, proc print, proc tabulate, macro definition, %if-%then-%else, proc lua, sas packages, call execute, data step, macro put | source | 58 |
| 189 | Running the CDISC Open Rules Engine (CORE) in BASE SAS | Lex Jansen | CDISC Conformance Rules are an integral part of the Foundational Standards and serve as the specific guidance to Industry for the correct implementation of the Standards in clinical studies. The overall goal of the CORE Initiative is to provide a governed set of unambiguous and executable Conformance Rules for each Foundational Standard, and to provide an open-source execution engine for the executable Rules which are available from the CDISC Library [1][2]. The source code of the CORE engine is available on the GitHub repository. A Command Line Interface (CLI) is available on the repository which allows users to run the rules under Windows, Mac, and Linux. If users want to run the Engine in their own Python environment or tooling, it can be implemented as it is available on PyPi (Python Package Index). For SAS users it is not always an option to run applications as a Command Line Interface. The presentation will begin with a brief overview of CDISC CORE | proc fcmp, proc step, data cleaning, controlled terminology, sas function, define-xml, excel, dataset, proc contents, macro definition, macro call, macro variable, cdisc | source presentation |
40 |
| 190 | SAS Macro Programming Tips and Techniques | Kirk Paul Lafler | The SAS Macro Language is a powerful tool for extending the capabilities of the SAS System. This presentation teaches essential macro coding concepts, programming techniques, and tips and tricks to help SAS users learn the basics of how the Macro language works. Using a collection of proven Macro Language coding techniques, attendees explore the application of how to write and process macro statements and parameters; replace text strings with macro (symbolic) variables; generate SAS code using macro techniques; manipulate macro variable values with macro functions; create and use global and local macro variables; construct logical expressions; interface the macro language with the SQL procedure; store and reuse macros; troubleshoot and debug macros; and develop efficient and portable macro language code |
macro facility, macro variable, proc step, proc sql, keyword parameter, macro parameter, %macro parameters, autocall macro, dictionary tables, macro definition, macro function, proc contents, &variable, options statement, macro loop | source | 11 |
| 191 | SAS Macro to Calculate Standardized Mean Difference | Tong Zhao, Tian Gu |
The standardized mean difference (SMD) is a measure of effect size between the two groups. It is widely used in the external controlled clinical trials studies, comparing the baseline characteristics of the treated group and the propensity-score (PS) weighted or propensity-score (PS) matched control group. This paper details the methodology for calculating SMD for both continuous and binary variables in such analyses. Additionally, we provide a SAS macro that implements these calculations directly from the underlying formulas, offering researchers a flexible alternative to standard procedures | macro definition, binary variable, sample | source | 2 |
| 192 | SAS Program for Backup Zipping | Tong Zhao | In everyday work, after completing a study or identifying outdated documents and datasets in folders, we often create zip archives to save memory and space. However, manually zipping dozens of folders individually- while retaining their original names-can be time- consuming. This SAS program automates the process by replacing folders with identically named zip files in the directory, all executed through a single batch run. This paper demonstrates the program's usage and provides a step-by-step explanation of the code. Additionally, it highlights how to integrate command-line tools into SAS, serving as a brief tutorial for those interested in combining external software with SAS programming | sas programming, macro put | source | 5 |
| 193 | Scoring Real-World Data Reliability for Clinical Investigations | James Joseph | data standard, insurance claims | source | 15 | |
| 194 | SDTM Transformation through Artificial Intelligence (AI) and Human in the Loop (HITL): Lessons Learnt from Abbvie Case Study | Aman Thukral, Sanjay Bhardwaj |
Clinical data transformation is an essential process in ensuring data quality, accuracy, and regulatory compliance in clinical trials. Traditional methods rely heavily on manual programming, which can be time- consuming and prone to human error as the programmer must have a deep understanding of evolving data standards. The integration of Machine Learning (ML) and Artificial Intelligence (AI) into programming structured datasets offers a promising solution to these challenges. This paper outlines the traditional approaches to data standardization, AbbVie's key steps in implementing these advanced technologies, and the efficiencies gained | clinical data, data transformation, machine learning, data standard, data standardization | source | 2 |
| 195 | Sensitivity Analysis for Overall Survival | Binal Mehta, Patel Mukesh |
Overall survival (OS) refers to the length of time from either the date of diagnosis or the start of treatment for a disease, such as cancer, during which patients diagnosed with the disease remain alive. It is an objective endpoint that directly evaluates the effectiveness of new treatments in prolonging patient life and is often a crucial criterion used in regulatory approvals for new treatments. Sensitivity analysis for overall survival is a statistical method used to assess how various assumptions and parameters such as handling of censoring, missing data, extreme data points, covariates and subgroups, or model components impact the results. The purpose of sensitivity analysis in this context is to gauge the reliability of conclusions drawn from survival data. This helps statisticians and clinicians determine whether their survival estimates remain robust under various conditions | overall survival, sensitivity analysis, survival analysis, adam dataset, clinical trial, proc phreg, outlier, regression analysis, cdisc | source | 13 |
| 196 | SET Statement Considered Harmful | Bartosz Jabłoński | The SET statement is the primary way SAS ○Rprogrammers access the data (I could bet $5, it is over 90% of cases). Since data processing in SAS is I/O oriented the majority of optimization can be done by reducing I/O, i.e., the proper use of the SET statement1 in the code. The article is dedicated to beginner SAS programmers and considered education focused. In the paper we present a group of examples showing how (mis)use of theSET statement can be (and is) harmful for data processing and how the grim situation can be fixed. Table of contents |
proc step, proc sql, proc sort, set statement, create table, sas programmer, proc append, proc transpose, data step, %if-%then-%else, sas packages, hash table, i/o optimization | source | 21 |
| 197 | Share your Macros and Programs with SAS Studio Steps and Flows | Jim Box, Pritesh Desai |
You've probably got a huge macro and programming library at your disposal, but no way of leveraging all of that capital in any way but inside a SAS program. We'll look at how using SAS Custom Steps and Flows will allow to unlock the full potential of your work. By leveraging these capabilities, non- programmers can seamlessly run analyses and other complex processes that you built with minimal training and intervention on your part. This integration not only enhances collaboration but also democratizes access to powerful analysis tools, enabling a broader audience to contribute to data-driven decision-making. Through practical examples and detailed guidance, this paper showcases the simplicity and efficiency of converting SAS macros into user-friendly tools, ultimately fostering an inclusive and efficient research environment | macro variable, sas studio, adverse events | source | 9 |
| 198 | Shining a Light on Adverse Event Monitoring with R Shiny | Abigail Zysk, Joe Lorenz |
In pharmacovigilance (PVG), timely and precise reporting of serious adverse events (SAEs) is vital for patient safety and regulatory compliance. To enhance the display and usability of SAE data, we developed an R Shiny dashboard for the automated reporting of SAEs. R Shiny offers several advantages over traditional static reporting for dynamic and interactive data visualization and reporting. Key features of the R Shiny dashboard include: 1. Real-time Data Integration: Seamlessly extract and process SAE data from existing SAS programs. 2. Interactive Visualization: Dynamic charts and graphs provide immediate insights into SAE trends. 3. Customizable Alerts: Configurable notification settings to alert users of specific SAE criteria. 4. Automated Reporting: Scheduled and on-demand report generation with automated email delivery. 5. User-Friendly Interface: An intuitive interface that accommodates users with varying levels of technical expertise | histogram, box plot, adverse events, data visualization, proc freq | source | 10 |
| 199 | Shiny & LLMs: Landscape and Applications in Pharma | Philip Bowsher | Shiny is impactful in the pharmaceutical industry as it helps people share statistical programming and analysis via a web-based interface. The interactive web-based capabilities help users explore analysis and see results quicker than in static output. Shiny is applied in drug discovery to submissions and beyond as highlighted in the article: https://posit.co/blog/shiny-use-cases-within-pharma/ Pharmaceutical organizations have built an exploration framework on top of Shiny for exploratory data analysis via teal: https://insightsengineering.github.io/teal/latest-tag/ The R in Pharma conference each year features talks and workshops on Shiny which can be seen here: https://www.youtube.com/c/RinPharma There are various use-cases for GenAI to further the power Shiny in pharma | statistical programmer, clinical programming | source | 17 |
| 200 | Simulating Optimal Sample Sizes for Canine Jaws Using SAS | Chary Akmyradov, Lida Gharibvand |
In the realm of veterinary clinical research, ensuring animal welfare while achieving statistically significant results is paramount. This study presents an advanced approach to sample size calculation for a clinical study involving canine subjects, with a focus on dental health. The unique design of this study employs dogs as both cases and controls by longitudinally comparing treated and untreated teeth within the same animal, thus minimizing the number of subjects required and reducing animal suffering. A pivotal aspect of this research is the optimization of the number of dogs and the number of teeth extracted per dog. The goal is to minimize both, ensuring minimal discomfort to participating animals. The teeth growth in dogs is monitored at three distinct time points to assess the development of treated versus untreated teeth. To achieve a robust and reliable study design, a simulation-based approach was adopted | sample, proc step, sample size, proc freq, proc glimmix, null hypothesis, power analysis, beta distribution, macro definition | source | 11 |
| 201 | Slamming Datasets Together, Without Hurt, Using SAS, R and Excel | David W. Franklin | Bringing two datasets together to get a better picture of data is a common action performed by programmers dealing with sets of data, usually to bring it into a form for either storage or analysis. This paper will look at one-to-one, one-to-many and many-to-many situations using SAS, including the common way using a MERGE statement but also touch on PROC FORMAT, PROC SQL, POINT and KEY options in a SET statement, and HASH tables. On our journey we will also touch on one-to-one, one- to-many and many-to-many using R, and the limited form of one one-to-one and one-to-many using EXCEL of merging data |
excel, proc step, merge statement, proc sql, sas log, set statement, hash table, proc format | source | 6 |
| 202 | Slice, Dice, Analyze: Revolutionizing SDTM cuts with datacutr package | Diego Madrigal Viquez | Processing SDTM data is a key step in clinical trial analysis and doing it efficiently can save significant time and effort. The "datacutr" package, built in R, provides a practical solution for applying cuts to SDTM domains. With different methods, it simplifies data preparation for analysis by automating much of the process. This article focuses on real-world examples of using "datacutr" to handle SDTM data. We will demonstrate step-by-step how the package works, showing how different cuts can be applied based on specific needs. By automating these tasks, "datacutr" reduces manual effort, minimizes errors, and speeds up the workflow, making it easier to prepare datasets for regulatory submissions or internal use. As an R-based tool, "datacutr" highlights the growing importance of R in the clinical industry. With its flexibility, open-source nature, and powerful capabilities for data manipulation and analysis, R is becoming an essential skill for programmers and statisticians | clinical trial, clinical programming, data wrangling, clinical cut, regulatory submission, cdisc | source | 6 |
| 203 | Statistical Programming Outputs Comparison and Reporting Using Python | Vinayak Mane, Tanusree Bhattacharyya, Adel Solanki, Anindita Bhattacharjee, Nibedita Hazra |
This paper showcases the functionality and workflow of an automated Python tool designed to compare different versions of clinical trial outputs, particularly in the context of statistical programming. This tool, named the "Outputs Comparison Tool," is a user-friendly executable (.exe) application that streamlines the process of comparing and analyzing the differences in original and revised outputs. Iterations of data sets, tables, listings, and figures (TLFs) are common in the programming workflow, often resulting from updates in work scope or analysis requirements. These iterations lead to multiple intermediate submissions for review. Ensuring consistency across outpu ts in these iterations is a challenging task for statistical programmers, typically performed manually, making the process both tedious and error-prone | excel, statistical programmer | source | 10 |
| 204 | Stop Making More Physical Copies of Your Data: A Modern Approach to Traceability and Fidelity | Anthony Chow, Robert Garelick |
The pharma industry faces significant challenges with data traceability and fidelity due to the pervasive creation of physical copies of data and derivatives that are uncontrolled. This practice diminishes data quality, increases regulatory risks, and creates inefficiencies in compliance and governance processes. Our approach proposes a paradigm shift: treating all datasets as traceable views derived from a single source of truth. By leveraging modern technologies such as data lakes, event streams, and unified analytics platforms, organizations can eliminate unnecessary redundancies, enhance traceability, and improve data fidelity. We highlight how tools like dbt and Apache Spark enable end-to-end logging of transformation logic, ensuring compliance and reproducibility. This framework offers key benefits, including eliminating data silos, enabling scalable automation, and fostering collaboration through a unified data platform | raw data, data transformation, analytics platform, data governance, data lineage, clinical data, data pipeline, cdisc | source | 8 |
| 205 | Strategies for Thriving During Change and Uncertainty | Maria Dalton | Frequent change and uncertainty have become a fact of life for life science and health research professionals. Examples of changes are organizational reorgs and upheavals, new programming languages, emerging technologies such as AI/ML, new resourcing models and global disruptions such as the COVID-19 pandemic. This presentation will first describe general techniques that everyone can practice to build resilience, optimism, and proactivity during periods of change and uncertainty. The presentation will then give advice to leaders on best practices for supporting and gui ding their teams during change. The presentation will focus on practical advice that has worked for the author and will include references to helpful resources | life sciences, pharmaceutical industry | source | 5 |
| 206 | Strategies to Encourage Adoption and Innovation in Statistical Programming | Archana Gundamraju | Pharmaceutical companies can focus on efficiency by being open to new tools and processes. It is vital to adapt and ensure a smooth transition from a traditional or familiar way of working to a new way of working. Sometimes, change is difficult and is resisted. This could be due to constraints such as timelines, budget, lack of time to train or a fear of the unknown. Incorporation of change in a team or a department requires proper planning, evaluation, and implementation. One can be an effective leader in Statistical Programming by combining technical skills and leadership skills with a growth mindset. One can also contribute to organizational growth by being receptive to the concerns of the team, providing proper guidance and giving feedback about tools or processes during adoption. This paper presents two strategies used within statistical programming teams to navigate changes and encourage innovation that could help in meeting the standards and improving efficiency | clinical programming, statistical programmer | source | 6 |
| 207 | Streamlining and Accelerating Clinical Research through AI and GenAI driven Insights and safety review of Medical and Scientific Literature | Rohit Kadam, Saurabh Das, Rajasekhar Gadde, Niketan Panchal, Alejandra Guerchicoff |
Literature search in the medical life sciences domain involves systematically exploring and reviewing existing academic and research literature to gather information, evidence, and insights related to a specific topic or research question and identify and report safety events. With the advancements in technology and rapid growth of Life Sciences domain, the amount of data and information generated every day is enormous. Weekly there are new releases and updates of medical journals and papers. To get the correct articles and papers it is important to identify and choose appropriate databases for the search. In the medical field, databases like PubMed, MEDLINE, Embase, and other databases are commonly used. Search is done on these databases using a custom search strategy. The relevant articles based on the search strategy are selected, and information from them is extracted stored in a structured manner | life sciences, clinical trial, data cleaning, clinical research | source | 6 |
| 208 | Streamlining BIMO and Patient Profile Generation: A Python-Based Semi-Automated Approach Integrated with CSR Development | Dmytro Skorba, Mykyta Vysotskyi |
Preparing Bioresearch Monitoring (BIMO) and Patient Profile (PP) listings traditionally occurs after Clinical Study Report (CSR) development, often leading to duplicate effort, inconsistencies, and time pressure. This paper presents an innovative semi-automated approach that integrates BIMO and PP requirements during the initial CSR development phase. By leveraging Python's capabilities alongside existing SAS infrastructure, we demonstrate how to transform CSR listings into FDA-compliant BIMO and PP outputs while maintaining data consistency and reducing validation burden. The approach combines organizational strategies for early planning with technical solutions for automated data transformation and formatting. Practical examples illustrate how this methodology significantly reduces development time, minimizes manual intervention, and ensures alignment across all submission deliverables | clinical data, clinical trial, adverse events, dataset, crf, data cleaning, out=, data wrangling | source | 15 |
| 209 | Streamlining Validation Review and SAS Program Management with R | Huei-Ling Chen | Unlike SAS, R treats nearly everything as an object that can be easily modified. In addition to familiar objects such as datasets, the R language can also work with lists, vectors, and matrices. This flexibility in handling various objects dramatically expedites the workflow. This paper illustrates how to leverage R language to import large SAS files into a list for rapid collective processing within seconds. It highlights three specific applications that optimize post-processing tasks: (1) examining large-scale SAS validation output files, (2) automating the trimming of SAS syntax text, and (3) adding SAS syntax text to append data attributes from a central location for all analysis datasets | proc compare, sas syntax, proc step, adam dataset, sas output, dataset, clinical trial, data cleaning, excel, attrib statement, cdisc | source | 11 |
| 210 | Streamlining Your Workflow: Creating Portable and Automated SAS Enterprise Guide Project Using Project Name | Chary Akmyradov | This paper presents a practical approach to enhancing SAS Enterprise Guide (EG) project management by leveraging the project's metadata - specifically the project name and directory path - to automate routine setup tasks. The method utilizes the Autoexec workflow, the DLCREATEDIR option, the automatic macro variable &_CLIENTPROJECTPATH, and key SAS functions such as dequote, find, and substr. The step-by-step guidance provided in this paper will demonstrate how to automate the f ollowing tasks: Define permanent libraries using portions of the SAS EG project name. Automatically assign and create (if not already existing) library folders based on the project directory. * Generate additional folders for exports, reports, and other project outputs. A notable advantage of this approach is its built-in adaptability: when a project is moved or executed from a different directory, the library paths are automatically updated | sas enterprise guide, macro variable, sas enterprise guide project, permanent library, libname statement, automatic macro variable, sas function, macro function | source | 8 |
| 211 | Sugar Rush: SQL Master Class for Pharma Professionals | Charu Shankar | Join Charu Shankar, a seasoned SAS presenter, for a dynamic presentation exploring SQL's sweet role in pharmaceutical data analysis, with a focus on diabetes clinical trials. This session tackles industry - specific challenges using sugar-rich datasets like DCCT, NHANES, and TDS, offering hands-on practice with real-world glucose data. Participants will learn to query, summarize, and report clinical data, including HbA1c levels, demographics, and treatment outcomes. Key skills covered: Data Exploration: Sifting through the sugar to assess diabetes progression. Data Preparation: Blending datasets into a smooth analytical base. Advanced SQL: Sweetening your queries with averages, correlations, and metrics. Data Reporting: Serving up clear insights on treatment outcomes. By the end, attendees will confidently analyze clinical trial data, uncover trends in sweetness, and drive data-driven decisions in diabetes research | proc sql, select, proc step | source | 9 |
| 212 | Super Learner for Predictive Modeling and Causal Analysis | Honghe Zhao, Clay Thompson, Michael Lamm |
Model misspecification is a common challenge in causal analysis with real-world observational data, where statistical models are used to understand the complex relationships among outcomes, exposures, and baseline characteristics. Misspecified models can easily lead to biased estimates of causal effects and incorrect conclusions. The super learner algorithm tackles this problem by enabling you to consider multiple candidate models with different specifications and combine them into a single, robust model. The super learner leverages the strengths of these candidate models to learn the complex relationships between the variables, reducing the risk of overfitting and model misspecification. This makes it well - suited for applications such as patient outcome predictions and causal analysis using real -world observational data, where complex interactions and dependencies among the variables are present | causal inference, potential outcomes, proc step, treatment effect, regression analysis, data cleaning, confidence interval, logistic regression, maximum likelihood estimation, response variable, average treatment effect, logistic distribution, feature selection, likelihood function, training set | source | 9 |
| 213 | Swanky Sankey Enhancements: Transforming a Graph with Pretty Curves to a Research Tool uncovering Deeper Scientific Insights | Siqi Wang, Toshio Kimura |
Sankey plots show the flow of data from one state to another over time. Existing implementations are available displaying the main bar graph with curvy paths flowing from one category to another1. However, reviewers are left wondering how many patients are going to and coming from each category. Additionally, the relative spacing on the x-axis representing time is not maintained; therefore, 4-week and 12-week intervals are represented with equal spacing. We propose the following enhancements to improve upon these shortcomings. First, we will introduce sidebars along with the option to display n and percent for the number of patients going to and coming from each category. This will proactively address the most asked question from clinical and medical writing colleagues. Second, we will use relative spacing for the x-axis representing time so that unequal time intervals will be appropriately displayed. For example, a 4-week interval will be one third the space of a 12- week interval | missing value, bar chart, proc sql, proc freq | source | 10 |
| 214 | The (ODS) Output of Your Desires: Creating Designer Reports and Data Sets | Louise S. Hadden | SAS procedures can convey an enormous amount of information - sometimes more information than is needed. Most SAS procedures generate ODS objects behind the scenes. SAS uses these objects with style templates that have custom buckets for certain types of output to produce the output that we see in all destinations (including the SAS listing). By tracing output objects and ODS templates using ODS TRACE (DOM) and by manipulating procedural output and ODS OUTPUT objects, we can pick and choose just the information that we want to see. We can then harness the power of SAS data management and reporting procedures to coalesce the information collected and present the information accurately and attractively |
proc contents, proc step, ods output, sas output, estimate, output, excel, data step, proc univariate, odds ratio, proc sql, proc surveyfreq, proc report | source | 12 |
| 215 | The Allowable Total Difference Zone: A construction method using the ATDzone SAS Macro | Jesse Albert Canchola, Natasha Oza |
When comparing two systems using the same item or "sample" to produce at least two "paired" results (one on each system), for example, a new versus an older system for a molecular assay, typical method comparison methodologies used are Ordinary Least Squares (OLS), Deming, or Passing-Bablok regression (Passing & Bablok, 1983), bias plots that include Bland-Altman and Krouwer bias plot analysis and Error Grid Analysis (EGA) (Passing & Bablok, 1983; Linnet, 1993; Altman & Bland, 1983; Bland & Altman, 1986; Clark et al., 1987; Parkes et al., 2000) | standard deviation, macro definition, regression analysis, sample | source | 20 |
| 216 | The Current State of Teaching Biostatistics in Academia: Challenges and Software Solutions | Lida Gharibvand | The traditional approaches to teaching and learning biostatistics have gone through evolutionary stages. This is mainly due to the advancement of computing and modern approaches of data analytics, including but not limited to the methodological impacts of statistical machine learning. However, the growth and impact of biostatistics programs, both at the undergraduate and graduate levels, heavily d e p e n d o n building strong and relevant foundations. It is from that perspective that it becomes necessary to further evaluate the interaction of the main areas of mathematics, statistics, and computing. In this paper, we discuss the relevance and importance of incorporating modern approaches to data analytics, specifically in conjunction with biostatistics training and research. Chiefly, we aim to discuss the importance of computing and statistical thinking, as the building blocks of ideas associated with tackling projects that have real-life implications | sample size, sample, missing value, missing not at random, machine learning, missing at random, proc power and sample size, data visualization, data science | source | 5 |
| 217 | The Many Use Cases of Standardized Data and Metadata | Sanjiv Ramalingam | Study Data Tabulation Model (SDTM) datasets are predominantly Clinical Study Report (CSR) driven and primarily created to support analysis dataset creation. The data flow is typically unidirectional. The SDTM group has pioneered the creation of SDTM datasets enabling creation of SDTM datasets within days of First-Patient-In (FPI) and automatic refreshes enabling wider use of standardized data across the organization. With this new way of working, the SDTM group is able to serve and enable efficiencies across multiple functions such as Data Management, Data Standards and Governance, Biomarker and Clinical Operations than just Statistical Programming. The use cases for each of these groups have been discussed in this paper | sdtm domain, raw data, cdisc | source | 4 |
| 218 | The Many Ways to Build Cohorts to Effectively Generate Real World Evidence and Bring Drugs to Patients Faster | Sherrine Eid, Mary Dolegowski |
The process of defining and building patient cohorts in pharma today involves a mix of automated and manual methods that leverage structured data, unstructured data, and advanced analytics. Each approach carries trade-offs between scalability, accuracy, interpretability, and resource requirements. The importance of these methods lies in their ability to produce reliable real-world evidence that informs clinical practice, supports regulatory decisions, and ultimately improves patient outcomes. Properly defining patient cohorts affects study validity and reliability as accurate definitions reduce bias and confounding, ensuring that the evidence generated is robust and valid. They enable meaningful comparisons between treatments and help in drawing reliable conclusions about effectiveness and safety. Regulators and payers increasingly rely on RWE to complement randomized clinical trials (RCTs) in decision-making | inner join, create table, macro variable | source | 8 |
| 219 | The Model Maketh the Metadata | Carlo Radovsky | Since well before the passage of FDASIA (FDA Safety and Innovation Act) in 2012 in the United States, which ultimately led to the US FDA requiring adherence to CDISC standards in the submission of clinical trials data, the biopharmaceutical industry has known what it had to do - find a way to streamline processes and solutions. For just as long, it has struggled with how to do so. The rallying cry has been for an automated end-to-end solution, which has proved to be an elusive unicorn, at best painfully, partially supported by commercial products and often stymied by internal silos and broad-based technical debt. While there are a number of proximate causes for this, automating the generation of submission datasets begins with a metadata reference, be it the published standards documents, spreadsheets, or a robust repository | unique key, data standard, adverse events, cdisc | source | 19 |
| 220 | The Show Must Go On: Best Practices for Submitting SDTM Data for Ongoing Studies | Kristin Kelly | Though the CDISC SDTM Implementation Guide provides advice on how to prepare SDTM datasets for completed studies, there is little guidance on what to do when the study is ongoing, leading to varied implementation practices across the industry. At times, it may be difficult for a regulatory reviewer to readily determine that a study is still in progress without looking in the Clinical Study Data Reviewer's Guide (cSDRG). The recent addition of the ONGOSIND (Ongoing Study Indicator) parameter in the FDA Study Data Technical Conformance Guide (sdTCG) allows sponsors to clearly specify within the data whether a study is ongoing. In this paper, some considerations for preparing domains such as Demographics (DM), Disposition (DS), and Trial Summary (TS) for an ongoing study as well as strategies to ensure data transparency across the SDTM submission package will be discussed | data cleaning, trial summary, clinical trial, sdtm domain, cdisc | source | 9 |
| 221 | The Winding Road to ADSL | Elizabeth Dennis, Grace Fawcett |
The typical study will use SDTM data as the sole origin for the ADaM datasets. ADSL is derived using SDTM.DM, EX, and other domains. But what's the best process to use when the roadmap to ADSL contains twists and turns? There are times when the typical process is not robust enough to produce a well-documented ADSL that meets all the analysis needs. This presentation will go through three scenarios where details of the study and the analysis require a more complicated process to produce ADSL: 1. The derivations for a flag variable (usually a population flag variable) are so complicated that an intermediate dataset is helpful. 2. The derivations for a flag variable (usually a population flag variable) require clinical adjudication. 3. There are multiple participations per subject | adam dataset, flag variable, efficacy population, data science, cdisc | source | 6 |
| 222 | The World is Not Enough: Base SAS Visualizations and Geolocations | Louise S. Hadden | Geographic processing in SAS has recently undergone some major changes: as of Version 9.4 Maintenance Release M5 many procedures formerly a part of SAS/GRAPH are now available in BASE SAS. At the same time, SAS Graphics have added some new procedures such as PROC SGMAP that build on the functionality of SAS/GRAPH's PROC GMAP and incorporate ODS graphics techniques including attribute maps and image annotation. This paper and poster will replicate a map of the world created by the author with SAS/GRAPH and PROC GMAP with the annotate facility using PROC SGMAP to map three different metrics on a map of the world. New SAS mapping and SG procedure techniques will be demonstrated, following Agent 007's adventures across the globe |
proc step, sas base, proc gmap | source | 12 |
| 223 | The {teal} Adoption Playbook: Strategies, Tools, and Learning Paths for Open Source | Vedha Viyash | As the industry adopts open-source technologies, pharma companies are exploring ways to integrate R- based solutions into their workflows. {teal}, an open-source framework for building interactive clinical data review applications in Shiny, offers a powerful alternative to proprietary tools. However, adoption comes with challenges- "adapting existing workflows, integrating clinical data sources, ensuring regulatory compliance, and upskilling teams to effectively use {teal}. This talk presents a practical playbook for clinical teams looking to adopt {teal}. We will discuss strategies to streamline data integration, including the creation of data connector packages tailored to organizational data structures. Participants will be introduced to key practices for creating flexible Shiny apps with {teal} while maintaining regulatory standards. A detailed learning path focused on R and {teal} skills will also be shared to help teams adopt and scale the solution effectively | clinical data, data cleaning, adam dataset, data review, cdisc | source | 8 |
| 224 | Time to Event Analysis from Sample Size Considerations to Results Interpretation in Simple Words | Iryna Kotenko | This paper was inspired by multiple requests from colleagues seeking guidance on the key considerations when performing time-to-event analysis. Thus, the paper is a response to those requests, offering a clear and complete overview of the process. It explains when and why this type of analysis is useful, how to decide on the right sample size, and what statistical methods can be used. It also provides practical tips for understanding the results and avoiding common mistakes. Real-world and industry-specific examples are included to make the concepts easy to understand, not just for statisticians but for professionals from various fields | sample size, sample, survival analysis, rank test, hazard ratio, log-rank test, proc phreg, kaplan-meier estimator | source | 8 |
| 225 | Time-to-Deterioration for Patients Reported Outcomes | Christine S Teng | In oncology studies, time-to-deterioration (TTD) in Patient-Reported Outcomes (PRO) assessments serves as an important endpoint, measuring how cancer treatment affects a patient's quality of life and symptom burden. TTD is typically defined as the duration from a specified starting point-such as the initiation of treatment or a baseline assessment-until a patient indicates a clinically decline in their health status. This decline may be reflected by heightened symptoms, diminished quality of life, or an increased need for medical intervention. The definitions of TTD can vary depending on the primary focus of the specific disease area, and the intended objectives of the assessment. The two common applied definitions of TTD for PROs evaluation are (1) time to first deterioration and (2) time to first confirmed deterioration. This paper seeks to discuss a method for designing ADaM analysis datasets (ADPRO/ADPROTTE) that effectively support both types of TTD analyses | data science, clinical trial, cdisc | source | 11 |
| 226 | TLFQC: A High-compatible R Shiny based Platform for Automated and Codeless TLFs Generation and Validation | Chen Ling, Yachen Wang |
SAS-R integration and transition is a trending topic in the industry, more and more companies are incorporating R programming. Even though many R functions have been developed, Tables, Listings, and Figures (TLFs) generating still needs substantial repetitive work and the use of R for validating SAS- generated TLFs is very limited. Also, it might be hard for SAS users with no R experience to directly code in R. Taking advantage of the open-source software R, we are able to automate the process to make it easy to use for everyone, hence improving efficiency. In this paper, we will introduce our application demo: TLFQC, which leverages the R Shiny framework to automate the generation and validation of standard TLF datasets in a Web app, with following features: TLFQC can generate multiple TLFs simultaneously with the required parameters entered Users can customize TLFs report with wording from table of contents (TOC), ensuring the final output adheres to requirements | data cleaning, adam dataset, adverse events, dataset, cdisc | source | 18 |
| 227 | Towards an Integrated Submission-Ready Data Pipeline: Unifying Compliance, Automation, and Open-Source Innovation | Shivani Gupta, Bhavin Busa |
Can we develop a fully integrated solution for regulatory submissions, one that aligns CDISC compliance checks (CDISC Open Rules Engine - CORE), regulatory guidance (e.g., Study Data Technical Conformance Guide from FDA and PMDA), CDISC Metadata Submission Guidelines (define.xml, aCRF), eCTD validation, and other key requirements into a seamless, automated workflow? Regulatory submissions are becoming increasingly complex, with stringent expectations for automation, compliance, and efficiency. We must ensure alignment with evolving regulatory standards while reducing manual effort and costly rework. At Clymb Clinical, we envision a comprehensive solution that integrates all submission components into a unified framework, leveraging open-source initiatives. By embedding automated validation, QC checks, and submission packaging directly into the dataset development lifecycle, we can achieve: 1. Regulatory-aligned, submission-ready data with built-in quality control. 2 | data cleaning, regulatory submission, cdisc | source | 5 |
| 228 | Traceability and AI for Improved Understanding, Communication and QC of Clinical Trials | Tomás Sabat Stofsel | Data standardization and clinical trial code generation, validation and maintenance are resource- and time-intensive. Statistical code complexity is enormous as one study may contain dozens of files with 1000s of cross references. This makes understanding how data is analyzed in a study non-trivial. In this talk, we explain how we can use a trial's SAS analysis logs and metadata into a graph that powers logical inference and AI to provide a new level of study understanding and validation. We discuss how code traceability can enable existing processes across the statistical analysis value chain: spanning study building, results validation, specification validation, study pooling, and define-xml automation, vastly simplifying processes across a complex stakeholder landscape | data cleaning, clinical trial, raw data, data science, excel, cdisc | source | 5 |
| 229 | Transitioning External Clinical Studies to Internal: A Framework for Knowledge Transfer and Operational Excellence | Chunqiu Xia, Hong (Lily) Zhang, Xiaohui Wang |
This paper presents a framework for transitioning ongoing clinical studies acquired from external pharmaceutical companies into internal statistical programming operations. The primary challenge lies in ensuring regulatory compliance with the CDISC (Clinical Data Interchange Standards Consortium) standards, a critical requirement for data integrity and regulatory submissions. To address this, the framework includes extensive efforts to align external data structures to internal formats, standardize deliverables such as Tables, Listings, and Figures (TLFs), and conduct rigorous compliance checks. Additional efforts involve creating centralized knowledge repositories, re-mapping external datasets, and tailoring statistical programming to internal requirements. Operational tools, such as delivery trackers and structured meetings, further enhance the efficiency and coordination of the transition process | data cleaning, clinical programming, clinical trial, adam dataset, data integrity, regulatory submission, adverse events, cdisc | source | 6 |
| 230 | Translation from SAS to R Using ChatGPT | David J. Bosak, Brian K. Varney |
Recent years have seen an explosion of use cases for generative AI. People are using AI to do research, write papers, summarize articles, and write code. At first, the quality of the output was not very good. Yet with each new release, the output is getting better and better. It is now feasible to perform tasks that previously would not be considered. In this paper, we will explore the topic of code translation from SAS to R using ChatGPT. The paper will first provide an overview of how to perform such a translation. We will then elaborate on a list of tips and tricks to make the machine translation better. Finally, we will point out some limitations of the technology and tell you what to watch out for. The paper will hopefully present you with enough information to try translating some code yourself | proc freq, proc report | source | 10 |
| 231 | Trying Out Positron: New IDE for Statistical Programming | Philip Bowsher | This paper will introduce a Pharmaverse workflow in Positron, an extensible, polyglot tool for writing code and exploring data, currently in Public Beta. The main Positron site for reference is: https://positron.posit.co/ Many pharmaceutical companies are shifting to an open-source backbone in Clinical Trials. Usually this effort includes creating a Next-Gen Statistical Computing Environments (SCE) where the focus is on open source languages for clinical reporting. A recent webinar by James Black discusses "The importance of the SCE in enabling our shift to open-source data science": https://pos.it/enable-oss James highlights the use of R and Python programming in the SCEs to support new hires as well as use-cases like data science and Real-World Evidence. Moreover, many pharmaceutical companies are updating data environments to modern platforms like Databricks, Snowflake, etc., where workflows are often Python-based | data science, adverse events | source | 10 |
| 232 | Understanding HIV: At-Risk Populations, Treatment, Prevention and Standardized Data Representation in HIV Studies | Jyoti (Jo) Agarwal | Human Immunodeficiency Virus (HIV) and its progression to Acquired Immunodeficiency Syndrome (AIDS) pose significant global health challenges. HIV is classified into two types, HIV-1 and HIV-2, with HIV-1 being the most common and virulent. The virus weakens the immune system by reducing CD4+ T -cell counts, increasing susceptibility to infections. Populations at high risk include sex workers, transgender individuals, and infants born to HIV-positive mothers. The standard of care for HIV treatment is Antiretroviral Therapy (ART), while Pre- Exposure Prophylaxis (PrEP) is used for prevention in high- risk individuals. Accurate diagnosis and management require extensive data collection, including immunogenicity tests, microbiology reports, and clinical event tracking | clinical trial, clinical data, clinical research, data governance, regulatory submission, likelihood function | source | 12 |
| 233 | Unleash Your Coding Potential: SAS PRX Functions for Next-Level String Manipulations | John M. LaBore | Explore the power of SAS PRX functions to elevate your string manipulation skills and enhance coding efficiency. These essential tools, available in both SAS 9 and SAS Viya, allow advanced SAS programmers to achieve complex data analysis with fewer lines of code and tackle challenges beyond traditional functions. Syntax examples and valuable resources are provided to help kickstart your usage | cas, sas viya, prxmatch, proc sql | source | 19 |
| 234 | Unleashing Oncology Revenue Insights: Advanced Forecasting Frameworks in Action | Naquan Ishman, Dave Kestner |
In the evolving landscape of oncology therapeutics, accurate revenue forecasting is critical yet challenging. This presentation introduces an innovative hybrid framework combining SAS and open- source tools to tackle complex forecasting needs, including dynamic competitive landscapes, patient flow modeling, and regulatory compliance. Through a live demo and case study, we showcase how this approach improved forecast accuracy for a recent oncology launch, addressing data privacy, version control, and algorith m validation challenges. Attendees will gain actionable insights to enhance forecasting processes, supporting informed decision- making for oncology product launches and strategic planning. Oncology revenue forecasting is challenging due to dynamic competit ive landscapes, complex patient flows, and strict regulatory requirements. Existing approaches often fail to integrate diverse datasets, adapt to market shifts, and ensure accuracy and transparency | data cleaning, machine learning, monte carlo, confidence interval, markov chain, transition matrix | source | 10 |
| 235 | Unlocking Insights: Comparative Analysis of CRF Changes with R Shiny | Mayank Singh | In the clinical research industry, Case Report Forms (CRFs) undergo multiple updates throughout the course of study execution. It is the responsibility of the Biostatistics and Programming group to ensure that the downstream programs remain synchronized with any modifications to the CRFs, including the addition of forms, updates to fields, and changes to code lists. However, identifying these CRF updates is a manual and labor-intensive process, often prone to errors that can result in overlooking critical changes. This paper presents an R Shiny application designed to facilitate the comparison of various versions of CRF metadata, whether within a single study or across studies. The application generates a comprehensive comparison report that is accessible via an R Shiny dashboard or available for download in EXCEL format. Additionally, it allows the team to utilize programming codes from similar studies, enhancing efficiency in their current research |
excel, clinical research | source | 5 |
| 236 | Unlocking Success: Lessons from Building R-Based Statistical Packages in Pharma | Sydney Hyde, Yirong Cao |
Packages are essential tools that enhance R programming's capabilities by incorporating custom functions, datasets, and fully compiled code. At BMS, we develop two types of statistical packages: one for statistical analysis and another for Health Authority submissions. BMS has developed robust, compliant, and efficient R-based statistical packages that meet the needs of both internal stakeholders and regulatory authorities. Through our experience in building R -based statistical packages, we have identified several key lessons. For example, ensuring accuracy, traceability, and compliance through validation, effective version control, and clear documentation is vital; secure data handling protects integrity and confidentiality; modular design and thorough documentation promote collaboration and ease maintenance ; a strong internal R user community enhances teamwork, knowledge sharing, and the development of statistical packages | data cleaning, clinical data, pharmaceutical industry, data science | source | 9 |
| 237 | Unveiling Paradoxical Pathways: A Counterintuitive Compass for Strategic Decision-Making | Anbu Damodaran | In the dynamic and unpredictable business landscape, strategic decision-making often hinges on our ability to anticipate and navigate counterintuitive outcomes. This paper explores the fascinating realm of statistical paradoxes - situations where seemingly sound logic leads to unexpected results. We examine a diverse range of paradoxes, including Simpson's Paradox, Berkson's Paradox, the Monty Hall Problem, and others, demonstrating their profound implications for effective management across various domains. From market analysis and resource allocation to risk management and competitive strategy, these paradoxes expose hidden biases and potential pitfalls in conventional decision- making processes | sample, numeric functions, null hypothesis, resource allocation, probability distribution, correlation, clinical trial, data integration, statistical programmer | source | 14 |
| 238 | Updating Define-XML packages: Tips and A Comprehensive Checklist | Qiong Wei, Ji Qi, Lixin Gao |
The Define-XML package, which provides a truthful representation of clinical trial data, is a critical component of clinical submissions to regulatory agencies such as the FDA and PMDA, in accordance with the electronic Common Technical Document (eCTD). However, as a study progresses, factors like protocol amendments, dataset changes, and new submission requirements often necessitate updates to an existing Define-XML package. This paper presents a comprehensive checklist designed to guide clinical submission professionals in updating Define-XML packages to ensure submission readiness and compliance with regulatory requirements | adam dataset, data cleaning, protocol amendment, clinical data, data science, controlled terminology, sdtm domain, clinical trial, cdisc | source | 10 |
| 239 | Use Gen AI to program rules in R & Python to generate and validate metadata for data standards and study specifications. | Priyanka Sawant, Kairav Tarmaster |
A common and frequent challenge with metadata is that it needs to be entered/created, and a human making these entries is time-consuming and error prone. A simple answer is that the metadata is created and validated based on rules well-defined by the industry (CDISC and Regulatory Conformance Rules), a company, a specific business function, or a system. For example, for a new CRF, study team members design and finalize the CRF structure and enter essential and minimal metadata for the CRF Items. The remaining metadata necessary to use the CRF in an EDC system can be automated by an (R / Python) application based on business rules. Similarly, define (.xml) specifications are mostly reverse-engineered from datasets and then manually updated. The define.xml is validated for conformance to CDISC and FDA rules, and errors and warnings are reported | data dictionary, metadata management, clinical trial, data cleaning, sdtm programming, data validation rules, edc, cdisc | source | 9 |
| 240 | Use of SAS Packages in Any Industry - Opportunities, Possibilities and Benefits | Bartosz Jabłoński | When working with SAS○Rcode, especially with a complex one, there is a moment when you decide to break it into small pieces. You create separate files for macros, formats/informats, for functions or data too. Eventually the code is ready and tested, and sooner or later you will want to share it with another SAS programmer . You have developed a program using local PC SAS, but the deployment is on a server with a different OS. Your code is complex (with dependencies such as multiple macros, formats, datasets, etc.) and is difficult to share. Often when you try to share code, the receiver will quickly encounter an error because of a missing macro, missing format, or whatever ...small challenge, isn't it? In this article we discuss a solution to the problem - the idea of SAS Packages, what they are, how to use and develop them, and how to make code-sharing a piece of cake | sas packages, sas enterprise guide project | source | 10 |
| 241 | Using SAS with Microsoft 365: A Programming Approach | Chris Hemedinger | In today's cloud-connected world, traditional methods of accessing EXCEL data from SAS are becoming obsolete. With more content stored in SharePoint Online and OneDrive (hosted in Microsoft 365), it can be challenging to get SAS to read these files and publish new ones to these locations. This paper guides you through the steps of connecting your SAS programs to Microsoft 365, enabling you to read and write files to SharePoint folders, OneDrive folders, and Microsoft Teams. You will learn how to use SAS to connect to Microsoft 365 using the Microsoft Graph APIs. Additionally, we will introduce SAS macros developed to simplify common tasks: listing your files, reading files into SAS, and publishing new files from SAS. By the end of this session, you will have the tools and knowledge to seamlessly integrate SAS with Microsoft 365, enhancing your data management and collaboration capabilities |
proc step, macro definition, proc http, sas viya, macro variable, proc import, sample, excel, proc sql | source | 14 |
| 242 | Validate the Code, not just the Data : A System for SAS program validation | Jayanth Iyengar | Regardless of the industry they work in, SAS programmers are focused on validating data, and devote a considerable amount of attention to the quality of data, whether its raw source data, submitted SAS data sets, or SAS output, including figures and listings. No less important is the validity of code and the SAS programs which extract, manipulate, and analyze data. Although code validity can be assessed through the SAS log, there other ways to produce metrics on code validity. This paper introduces a system for SAS program validation which produces useful information on lines of code, number of data steps, total run and CPU time and other metrics for project-related SAS programs | cpu time, dataset, proc step, sas log, proc print, proc sql, excel, key performance indicator, proc import, flag variable, macro variable, txt, call symput, proc transpose, xls, input statement, proc contents, proc sort, if-then-else, create table, fullstimer, data step, data cleaning | source | 38 |
| 243 | Visualize High Dimension Data Using t-SNE | Jun Yang | We introduce a novel technique named "t-SNE", designed for visualizing high-dimensional data by assigning each data point a location in a two or three-dimensional map. This method, a variant of Stochastic Neighbor Embedding (Hinton and Roweis, 2002), offers improved optimization ease and generates markedly superior visualizations by embedding the points from high dimension to low dimension trying to preserve the neighborhood of that point. t-SNE excels in creating a unified map that unveils structures across various scales, a crucial attribute when dealing with high-dimensional data distributed across distinct yet interrelated low-dimensional manifolds, such as images depicting objects from diverse classes and viewpoints. To address the challenge of visualizing the structure within large datasets, we demonstrate how t-SNE leverages random walks on neighborhood graphs | proc princomp, dimensionality reduction | source | 11 |
| 244 | Visualizing oncology data through 3D bar charts in R and Python | Girish Kankipati, Venkatesulu Salla |
3D bar charts are powerful tools for visualizing complex data sets, particularly in clinical trial analyses. This paper explores the use of R and Python for creating 3D bar charts to effectively present oncology data, facilitating better analysis and decision making. The visualization of clinical trial results and patient outcomes data in oncology often requires sophisticated tools to highlight patterns and trends. R and Python, known for their robust data analysis and visualization capabilities which provi de versatile libraries such as ggplot2, plotly, and matplotlib, are excellent options to support the development of 3D visualizations. This paper demonstrates practical workflows for implementing 3D bar charts in both programming languages, including data preparation, chart customization, and advanced interactivity | bar chart, treatment arm, clinical trial, scatter plot, data visualization, procedures, complete response, data science, pd, standard deviation, sample, histogram | source | 18 |
| 245 | Visually Exploring Kaplan-Meier Curves Using SAS GTL and R survminer | Yang Gao | Kaplan-Meier curves are commonly used to visually summarize time-to-event data and present primary trial findings in clinical trials. A Kaplan-Meier curve shows the survival probability of an event at a certain time interval. In the SAS system, PROC LIFETEST and SGPLOT procedures are used to generate simple survival plots. SAS programmers can use SAS Graph Template Language (GTL) directly to design and customize their graphs using the TEMPLATE and SGRENDER procedures. In addition to SAS, R, a free programming language, is widely used for data manipulation, statistical modeling, and visualization. R has highly advanced graphical capabilities. This paper illustrates the procedures to generate Kaplan- Meier plots using two approaches of SAS GTL and survminer R package. By providing methods in both languages, readers have options when creating Kaplan-Meier curves for regulatory submission |
kaplan-meier plot, overall survival | source | 5 |
| 246 | What comes first, the chicken (ADSL) or the egg (ADNCA)? Modularize your covariate creation to support flexible analysis dataset implementation! | David C. Izard | Good Programming Practice dictates that you should create a variable once and then share it to other locations where it is needed. In the context of analysis datasets following the CDISC ADaM standard, this typically involves creating your covariates during the implementation of the ADSL dataset and then merging them on to other analysis datasets at subsequent points in the development sequence. ADNCA, the ADaM BDS dataset designed to support non-compartmental PK analysis, would utilize these covariates from ADSL but is usually needed and implemented before ADSL is available. This paper explores modular covariate creation and how it supports both the subtle details of covariate differences between ADSL and ADNCA and good programming practices | adam dataset, compartmental analysis, pk parameter, in=, key, cdisc | source | 6 |
| 247 | What's black and white and sheds all over? The Python Pandas DataFrame, the Open-Source Data Structure Supplanting the SAS Data Set | Troy Martin Hughes | Python is a general-purpose, object-oriented programming (OOP) language, consistently rated among the most popular and widely utilized languages, owing to powerful processing, user -friendly syntax, and an abundant open-source community of developers. The Pandas library has become Python's predominant analytic toolkit , in large part due to the flexibility and success of its principal built-in data structure, the Pandas DataFrame. Akin to the SAS data set, the DataFrame stores tabular data in columns and rows (i.e., variables and observations, in SAS parlance). And j ust as built -in SAS procedures, functions, subroutines, and statements manipulate and transform SAS data sets to deliver analytic insight and business value, so, too, do built-in Pandas methods, functions, and statements deliver similar functionality. However, where the similarities end, the DataFrame inarguably outshines and outpaces the SAS data set | dataset, sas base, user-defined format, data transformation, master data management, proc sql, proc step, macro variable | source | 17 |
| 248 | When PROC SORT Isn-t Enough: Implementing Horizontal Sorting in SAS | Krutika Parvatikar, Jeff Xia |
Sorting data is a fundamental task in SAS, typically handled using "PROC SORT". However, certain scenarios require sorting values within an individual observation rather than across rows. This paper explores horizontal sorting techniques in SAS, including array-based sorting and bubble sorting, to address such challenges. A key application in clinical research involves automating the assembly of citations for Tables, Listings, and Figures (TLFs), linking ADaM and SDTM datasets for streamlined review and improved data traceability. By demonstrating innovative sorting approaches, this paper highlights how tailored techniques can enhance efficiency and expand SAS's capabilities in handling complex data scenarios |
call sortc, sdtm domain, proc sort, adam dataset, proc step, if-then-else, cdisc | source | 5 |
| 249 | Which ADaM Data Structure Is Most Appropriate? Gray Areas in BDS and OCCDS. | Veronica Gonzalez | Choosing the ADaM dataset structure, based on the analysis requirements, is usually straightforward. Statistical analyses involving change from baseline, like those from Lab or Vital Signs, can only be done in a ADaM Basic Dataset Structure (BDS) dataset. Analyses that involve counts/frequencies of hierarchical dictionary data, like Adverse Events, Concomitant Medications, and Medical History, can only be completed in ADaM Occurrence Data Structures (OCCDS) dataset. However, there are times that the ADaM structure needed to support the statistical analysis is not well defined and can be completed using either BDS or OCCDS. This paper outlines an instance where both BDS and OCCDS ADaM datasets could be appropriate to achieve the statistical analysis | adam dataset, data science, medical history, sdtm domain, change from baseline, adverse events, raw data, proc freq, data cleaning, cdisc | source | 15 |
| 250 | Why SASSY? | David J. Bosak | R has several weaknesses in regards to its use in the pharmaceutical industry. It lacks many of the basic capabilities of SAS. It is also numerically inconsistent with SAS. R is furthermore conceptually very different from SAS, and quite difficult for a SAS programmer to learn and use. Lastly, the stability and backward compatibility enjoyed by SAS programmers generally does not exist R. Most R package developers do not give priority to these values. The purpose of this paper is to explain how the SASS Y system of R packages can solve all of these problems. The paper will first clarify the above problems. It will then give a quick overview of the SASSY ecosystem. Finally, the paper will elaborate on how SASSY addresses the above issues in a very simple and direct way. The end result is a small, stable set of R packages that replicates the basic functionality of SAS, without any of the weaknesses normally associated with R | sas programmer, sample | source | 13 |
| 251 | Worried about that Second Date with ISO? Using PROC FCMP to Convert and Impute ISO 8601 Dates to Numeric Dates | Richann J. Watson | Within the life sciences, programmers often find themselves doing a lot of dating - matching, converting between character and numeric values, and imputing missing components. Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) domains have implemented the use of the International Organization for Standardization (ISO) format, ISO 8601, for datetimes. These dates are stored as character strings with missing components denoted using a single hyphen. Although this format helps to standardize how dates and times are captured so that there is no confusion as to what the date represents, it leaves a longing for something more compatible for analysis purposes: determining durations and number of days from a reference point. The conversion of ISO dates to a numeric format requires a serious commitment, especially when partial ISO dates require imputations | imputation, proc fcmp, proc step, return, sas function, adverse events, options statement, cdisc | source | 19 |
| 252 | Writing SAS MACROs in R? R functions can help! | Chen Ling, Yachen Wang |
Repeated programming is an annoying task and will greatly impact the work efficiency. To avoid repetitive work, standard macros in SAS are developed. Currently, many companies are exploring the transition from SAS to R and programmers are starting to embrace R functions, similar to SAS macros, to handle repetitive programming, hence reduce workload and improve efficiency. This paper provides a detailed comparison of SAS MACROs and R functions, demonstrating how R functions can serve as an effective alternative for creating dynamic, reusable and robust code. In this paper, the first section explores foundational aspects, comparing SAS MACROs and R functions in terms of overall structure and calling methods, the usage of parameters or arguments. This section also discusses lazy evaluation in setting default values, scoping rules, and implementing conditional logic. Both example SAS and R codes are provided to illustrate functionalities | symbol table, macro definition, macro variable, local symbol table, global symbol table, if-then-else, adverse events, macro put | source | 17 |
| 253 | You don-t have to handle the truth! Three Things to Know about Synthetic Data | Catherine Briggs, S. Robert Collins, Sundaresh Sankaran |
Organizations face challenges to analytics innovation when confronted with data that are sensitive, restricted, imbalanced in classes or insufficient in volume. Synthetic data generation is an effective tool to help mitigate these challenges. First, we outline use cases for synthetic data, such as providing researchers more freedom in using novel methods, collaboration, and data sharing without the fear of violating data privacy regulations. Additionally, this approach can also be used to "scale up" a small population or underrepresented segment of a dataset when additional source data may not be available. Second, we discuss methods for generating, evaluating, and testing synthetic data for potential privacy leaks. Legacy anonymization methods diffuse the fidelity and utility of the source data while being prone to reversal attacks. We show techniques and results in multiple coding environments to mitigate these risks | raw data, sample, machine learning, insurance claims, clinical trial, life sciences | source | 7 |
| 254 | You've Got Options: Five-Star SAS System Option Hacks | Louise S. Hadden | SAS provides myriad opportunities for customizing programs and processes, including a wide variety of system options that can control and enhance SAS code from start to finish. This paper and presentation demonstrates methods of obtaining information on SAS system options and moves on to fully explicate ten SAS system option hacks, from COMPRESS to XWAIT. System options are highly dependent on platforms, security concerns, SAS versions and products: dependencies and defaults will be discussed. SAS practitioners will gain a deeper understanding of the powerful SAS system options they have seen, used, and automatically included in their code. This presentation is suitable for all skill and experience levels; platform and implementation differences are part of the discussion | options statement, proc step, proc options | source | 11 |
| 255 | Zero to Hero: The Making of a Comprehensive R Shiny Figures App | Yi Guo, Matthew Salzano, Nicholas Sun, Sean Healey |
Creation of an interactive R Shiny application from scratch may seem like assembling a complex puzzle, but with the right approach, it becomes an achievable and rewarding journey. This paper will take you through the development process for an enterprise-level application designed for the generation of high-quality figures for clinical research and development, including publications, presentations and regulatory submissions. Development was done with a unique eye towards integration, as a small step in a larger transformative and integrated strategy of delivering dynamic outputs. The app utilized the R Shiny framework and Golem's modular architecture to deliver a no-code interface for users to create customizable and dynamic visualizations from clinical data. It is centered around three core types of modules: data upload, figure generation, and download | forest plot, feature selection, box plot, swimmer plot, data visualization, clinical data, clinical research, line chart, regulatory submission | source | 30 |
| 256 | {sdtm.oak} V0.1 on CRAN, sponsored by CDISC COSA, part of Pharmaverse, is an EDC and Data Standard agnostic solution for developing SDTM datasets in R. | Rammprasad Ganapathy | {sdtm.oak} is an EDC (Electronic Data Capture systems) and Data Standard agnostic solution that enables the pharmaceutical programming community to develop CDISC SDTM datasets in R. The reusable algorithms concept in {sdtm.oak} provides a framework for modular programming and also can potentially automate SDTM creation based on the standard SDTM spec. INTRODUCTION {sdtm.oak} v0.1 is now available on CRAN. In this paper, we will introduce the package and showcase its features and present a roadmap for future releases. {sdtm.oak} is developed in collaboration with volunteers from several companies, including Roche, Pfizer, GSK, Pattern Institute, Transition Technologies Science, and Atorus Research. {sdtm.oak} is also sponsored by CDISC COSA with a vision of being part of CDISC 360 to address end-to-end standards development and implementation. IN THIS RELEASE The v0.1 release of {sdtm.oak} users can create the majority of the SDTM domains | sdtm domain, sdtm programming, edc, cdisc | source | 5 |
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