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BUGSPharmaSUG 2024 Papers - Index
197 papers. Source: PharmaSUG (Pharmaceutical Software Users Group) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | "Prompt it", not "Google it" : Prompt Engineering for Statistical Programmers and Biostatisticians | Kevin Lee | Since its release, ChatGPT has rapidly gained popularity, reaching 100 million users within 2 months. Even a new concept has emerged : "Prompt it" is now the new "Google it". Research shows ChatGPT users complete projects 25% faster. The paper is written for Statistical Programmers and Biostatisticians who want to improve their productivity and efficiency by using ChatGPT prompts better. The paper explores the pivotal role of prompts in enhancing the performance and versatility of ChatGPT or other Large Language Model. The paper shows how Statistical Programmers and Biostatistician utilize ChatGPT's capabilities and benefits such as the content development (e.g., emails, images), search for the information, Programming assistance in R, SAS and Python, Result Interpretation and many more. The paper also elucidates the distinctive advantages of employing prompts over traditional search methods. It emphasizes the unique characteristics of prompt engineering in ChatGPT | sas programmer, statistical programmer | source | 4 |
| 2 | A 'Shiny' New Perspective: Unveiling Next-Generation Patient Profiles for Medical and Safety Monitoring | Helena Belloff, William Lee, Melanie Hullings |
In clinical trials, patient profile listings are vital for medical and safety monitoring, offering a dynamic visual representation of individual patient information. Reliance on manual methods to coalesce and analyze patient data for these listings has posed challenges in data integration, speed, interactivity, and flexibility. Addressing these issues, our Clinical Data Science team has developed an innovative Patient Profiles R Shiny app, revolutionizing patient data visualization and reporting in clinical trials. The Patient Profiles R Shiny app outperforms the manual workflow through seamless integration of data from various clinical trial sources, enhancing coherence and utility for medical and safety monitoring. It effectively displays longitudinal data, enabling the tracking of temporal changes and the identification of correlations between medical history, demographics, adverse events, lab results, and other patient information | clinical trial, data visualization, adverse events, clinical data, data science, data integration, raw data, concomitant medications | source | 8 |
| 3 | A Change is Gonna Come: Maintaining Company Culture, Managing Time Zones, and Integrating Teams after a Global Acquisition | Lydia King | As a mid-sized CRO that prides itself on having a healthy company culture with emphasis on work-life balance, our US-based Biometrics team was quite good at 'running the engine' with our portfolio of clinical trial clients. And although expansion had been occurring for several years as part of the larger company strategy to build out global infrastructure and support specific client needs, there had been minimal impact to our department. In 2023, Catalyst executed the next stage of the growth plan when we acquired a Boston based Biometrics company with primary operations from India. We were now immersed in integration activities. So how did we keep that company culture and not fall victim to our fear of change? While it is still very much a work-in progress, this paper will outline the important strategies put into place to ensure a successful transition | clinical research, clinical trial, clinical programming | source | 3 |
| 4 | A Deep Dive into Enhancing SAS/GRAPH and SG Procedural Output with Templates, Styles, Attributes, and Annotation | Louise S. Hadden | Enhancing output from SAS/GRAPH has been the subject of many a SAS paper over the years, including my own and those written with co-authors. The more recent graphic output from PROC SGPLOT and the recently released PROC SGMAP is often "camera-ready" without any user intervention, but occasionally there is a need for additional customization. SAS/GRAPH is a separate SAS product for which a specific license is required, and newer SAS maps (GfK Geomarketing) are available with a SAS/GRAPH license. In the past, along with SAS/GRAPH maps, all mapping procedures associated with SAS/GRAPH were only available to those with a SAS/GRAPH license. As of SAS 9.4 M6, all relevant mapping procedures have been made available in BASE SAS, which is a rich resource for SAS users, and in SAS 9.4 M7, further enhancements were provided |
proc step, proc sgplot, sas base, proc greplay, histogram, sample, bubble plot, proc gmap, output delivery system | source | 16 |
| 5 | A fear of missing out and a fear of messing up : A Strategic Roadmap for ChatGPT Integration at Company Level | Kevin Lee | Does your organization allow ChatGPT at work? The answer might depend on where you work. Many organizations do not allow ChatGPT at work. The truth is that for the organizations, ChatGPT is a fear of missing out and a fear of messing up. But, just like any other past new technologies such as Cloud computing and social media, the organizations eventually integrate ChatGPT or other Large Language Model (LLM). This paper is for those especially Biometrics who want to initiate ChatGPT integration at work. This paper presents how Biometric department can lead the integration of LLM, focusing on the exemplary model ChatGPT, across an entire enterprise, even in situations where the organization restricts or prohibits ChatGPT usage at work. The roadmap outlines key stages, starting with an introduction to LLM and ChatGPT, followed by potential risks and concerns and the benefits and diverse use cases | proof of concept, privacy | source | 4 |
| 6 | A Framework for Risk-Based Oversight for Fully Outsourced Clinical Studies | Anbu Damodaran, AstraZeneca Rare |
In recent years, the pharmaceutical industry has seen a significant rise in outsourcing clinical trials to Contract Research Organizations (CROs). Estimates suggest that 75% to 80% of the biopharmaceutical industry's R&D costs are outsourced, highlighting the prevalence of this practice 1,2,3. These trials, spanning various therapeutic areas, are entrusted to multiple CRO partners. The oversight of these diverse CROs, with their varied practices, and the siloed approach by Therapeutic Area (TA) functional leads from the sponsor, can lead to inconsistent deliverables. This created a dire need for more consistent and effective CRO oversight. In this paper, we describe a new approach to CRO oversight that was implemented by a pharmaceutical company. The new approach involved the creation of a new group that was responsible for overseeing all outsourced studies, regardless of therapeutic area. This new group created a framework for risk-based oversight plan | clinical trial, execution phase | source | 7 |
| 7 | A Gentle Introduction to SAS Packages | Bartosz Jabłoński | When working with SAS○Rcode, especially when it becomes more and more complex, there is a point in time when a developer decides to break it into small pieces. The developer creates separate files for macros, formats/informats, and for functions or data too. Eventually the code is ready and tested and sooner or later you will want to share code with another SAS programmer . Maybe a friend has written a bunch of cool macros that will help you get your work done faster . Or maybe you have written a pack of functions that would be useful to your friend. There is a chance you have developed a program using local PC SAS, and you want to deploy it to a server , perhaps with a different OS. If your code is complex (with dependencies such as multiple macros, formats, datasets, etc.), it can be difficult to share. Often when you try to share code, the re- ceiver will quickly encounter an error because of a missing helper macro, missing format, or whatever | sas enterprise guide, sas packages | source | 2 |
| 8 | A New Approach to Automating the Creation of the Subject Visits (SV) Domain | Xiangchen (Bob) Cui, Jessie Wang, Min Chen |
The creation of the subject visits (SV) domain is one of the most challenging tasks of SDTM programming. Aside from the small portion of mapping from raw dataset variables to SV variables, SV programming mainly consists of a more complex derivation process, which is totally different from that of other SDTM domains. The dynamic parts of the SV programming process, such as identifying raw datasets and their variables with both date/time and clinical visits, cause manual development of a SAS program to be time-consuming and error prone. Hence, automating its code generation would achieve and enhance efficiency and accuracy. This paper will present a new approach for SV automation based on the SDTM automation done in our previous paper, which leveraged CRF specifications from an EDC database and SDTM standards [1] | time variable, date variable, subject visits, sdtm domain, dataset, raw data, sdtm programming, data step, data cleaning, macro call, data review, sample, clinical data, macro variable, permanent dataset, cdisc | source | 24 |
| 9 | A New Way to Automate Data Validation with Pinnacle 21 Enterprise CLI in LSAF | Crystal Cheng | Pinnacle 21 Enterprise is a software that provides checks on the data compliance with CDISC standards, control terminology and dictionaries when sponsors preparing clinical data submission to regulatory agencies. By validating clinical data early and frequently during the conduction of the clinica l trial, it helps users discover data issues and address data issues in advance, ensuring the quality of submission data. There are different ways to execute validations in P21 Enterprise. Users can either manually run the validation via the P21 user interface or, for a more automated process, execute a process flow in SAS Life Science Analytics Framework (LSAF) to invoke the Enterprise Command Line Interface(ECLI) from | data cleaning, raw data, clinical research, life sciences, proc http, proc freq, proc step, cdisc | source | 7 |
| 10 | A Practical Approach to Automating SDTM Using a Metadata-Driven Method That Leverages CRF Specifications and SDTM Standards | Xiangchen (Bob) Cui, Min Chen, Jessie Wang |
Automated SDTM generation has several benefits, including efficiency, accuracy, compliance with regulatory requirements, and the speeding up of the data analysis process. However, due to the dissimilarity and varying complexity of different CRFs, SDTM domains, and eSource systems among different studies, development of a tool to automate SDTM has been a challenging task for sponsors, CROs, and EDC service providers. We propose a new approach in automatic generation of SAS code for SDTM. A SAS-based macro is developed based on CRF specifications from an EDC database and SDTM standards. Our approach is user-friendly with high transparency, easily scalable to multiple studies, and especially useful for relatively smaller sponsors and CROs, for there is no requirement to standardize CRFs and raw dataset variables' attributes (which is the best practice but can be too work-intensive) and no required expertise in other computer languages | sdtm programming, sdtm domain, data cleaning, dataset, macro definition, clinical trial, macro call, sample, independent qc, sdtm programmer, cdisc | source | 23 |
| 11 | A Programmer-s Insight into an Alternative to TQT Study Data Submission | Hiba Najeeb, Raghavender Ranga |
We detail the submission process for Phase I concentration-QTc data to regulatory agency, seeking an alternate approach from conducting a separate Thorough QT/QTc (TQT) study. A waiver from the TQT is significant because it saves time by not requiring a separate study, which is critical to getting therapies to patients faster. The submission datasets to support the QT evaluation align with the FDA Technical specifications document for Submitting Clinical Trial Datasets for Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential of Drugs and CDISC TAUG-QT standards. The submission package also adheres to the FDA Technical Conformance Guide and QT Evaluation Report Submission checklist. This paper delves into the creation of a submission package utilizing Continuous Holter ECG and PK data from Phase I randomized placebo-controlled dose escalation studies | adam dataset, trial summary, clinical trial, phase i, dose escalation, clinical research, data science, cdisc | source | 7 |
| 12 | A quick guide to SDTM and ADaM mapping of liquid Oncology Endpoints. | Swaroop Kumar Koduri, Shashikant Kumar, Sathaiah Sanga |
Cancer is a disease where some of the body's cells mutate, grow out of control, and spread to other body parts. The mutated cells possess the ability to infiltrate and destroy healthy body tissue all over the body. Liquid Tumors (Blood Cancer) are commonly occurring in bone marrow and the lymphatic system. In oncology clinical trials, response and progression is key to measuring survival and remission rates. In accordance with the response criteria guidelines, oncology studies are also divided into one of three subtypes. The first sub type, Solid Tumor study, usually follows RECIST (Response Evaluation Criteria in Solid Tumor) or irRECIST (immune-related RECIST). The second sub type, Lymphoma study, usually follows Cheson 1997 or 2007. Lastly, Leukemia studi es follow study specific guidelines (e.g., IWCLL for Chronic Lymphocytic Leukemia) | clinical trial, disease response, tumor identification, cdisc | source | 10 |
| 13 | A R Markdown Structure for Automatically Generating Presentation Slides | Junze Zhang, Chuanhai Tang, Xiaohui Wang |
The paper introduces a method to structure R Markdown, separating the code to define the output format and the code to conduct the analysis and compile the output using the predefined format. This paper will give an example of generating PowerPoint slides, though this method can be appli ed to various report formats. Traditionally, R Markdown is an authoring framework executing code and generating reports simultaneously. This method provides additional structure to the traditional method, which helps eradicate the work of manual data transfer to slides. For example, instead of regularly compiling slides for upper management to review the study result, statistical programmers can help stats utilize this method to compile the R markdown for stats to simply insert the code and output the result in the desired PowerPoint slides format. Consequently, this generates updated slides with the most recent data, requiring little to no modifications | statistical programmer, data wrangling | source | 9 |
| 14 | A SAS Macro Approach: Defining Line of Therapy Using Real-World Data in Oncology | Yu Feng | In oncology, Line of Therapy refers to the specific phase or sequence of treatment that a patient undergoes in the management of their cancer. Cancer treatment is often organized into sequential lines of therapy, each representing a distinct phase or set of interventions. However, most healthcare databases lack explicit information on treatment line of therapy. This paper introduces an innovative SAS macro designed to depict patient treatment regimens in oncology using a defined algorithm. The algorithm initially defines the treatment regimen within a specified timeframe of the index date. Stopping drugs from the combination regimen does not advance the treatment line, but adding a new drug will start the next line of therapy. If the duration between two cycles, lacking any chemotherapy or biologic regimen, exceeds the allowable gap days, a new line of therapy is instituted. The proposed SAS macro integrates an embedded macro to create types and flags, distinguishing various scenarios | sample, data step | source | 7 |
| 15 | A Simple Way to Make Adaptive Pages in Listings and Tables | Yi Guo | Generating listings and tables is an essential skill for every statistical programmer. The task of optimizing the display of listings and tables can be a challenging one for new programmers. This is because the true length of a variable can vary significantly between patient records, consequently affecting the number of pages in the listing, file size, loading time, and empty space on a page. Similarly, in tables, the number of distinct values of a categorical variable can vary as a study matures, and summarizing such categorical variables without affecting the page break can also be challenging - ideally, we would want to summarize a categorical variable on the same page when space allows. In this paper, we provide a simple algorithm that calculates the maximum number of observations to display on each page with an aim to optimize the display, the number of pages, the file size, and the loading time | standard deviation, nominal variable, proc report, proc step | source | 9 |
| 16 | A Tool for Automated Comparison of Core Variables Across ADaM Specifications Files | Amy Zhang, Huei-Ling Chen |
A specifications file serves as fundamental guidance for creating CDISC Analysis Dataset Model (ADaM) datasets. Dataset variables and their associated attributes in the specifications file have predefined standards. In particular, the core attribute is essential; per CDISC IG, the Core column defines whether a variable is required, conditionally required, or permissible. When preparing the ADaM specifications for a study, the programmer frequently encounters the task of ensuring the core variable categorization follows ADaMIG standards, company standards, or other protocols of the same compound or indication. To ease this process, having a tool to quickly compare the list of core variables across standards or studies can help facilitate ADaM dataset preparation. Storing these specifications in an EXCEL file is a common approach. Some existing software tools provide spreadsheet comparisons, but the findings are often overwhelming to digest |
adam dataset, excel, dataset, flag variable, cdisc | source | 6 |
| 17 | aCRF Copilot: Pioneering AI/ML Assisted CRF Annotation for Enhanced Clinical Data Management Efficiency | Chaitanya Pradeep Repaka, Santhosh Karra |
In clinical programming, the aCRF Copilot, leveraging Artificial Intelligence (AI), Natural language processing (NLP) and Machine Learning (ML), streamlines Case Report Form (CRF) annotation. It suggests and adds SDTM annotations and SDTM domain bookmarks to the aCRF, following CDISC guidelines. This reduces the time and manual effort traditionally needed in CRF annotation by intelligently matching aCRF text with current SDTM standards. The tool supports various domains like Adverse Events, Vital Signs etc., It has shown a ~70% accuracy (~92% Precision, ~62% Recall - ' ML metrics) in preliminary tests, expected to improve with user feedback loop. The aCRF Copilot has easy -to-use interface complete with a built-in mentor chatbot built with fine-tuned State of the art LLMs-Large language models (Mistral 7b, Zephyr 7b) designed to aid both beginner and expert users with different levels of SDTM knowledge ensuring data security and compliance with data protection regulations | clinical data, controlled terminology, vital signs, clinical data management, clinical trial, data governance, cdisc | source | 7 |
| 18 | ADaM Design for Prostate Cancer Efficacy Endpoints Based on PCWG3 | Lihui Deng, Kylie Fan, Jia Li |
Unlike other types of solid tumors that use the RECIST 1.1 [1] tumor response criteria, due to the particularity of prostate cancer, some common oncology efficacy endpoints, such as rPFS, ORR, time to response, and duration of response are usually based on the PCWG3 criteria [2]. Additionally, other specific prostate cancer endpoints like PSA response rate and time to PSA progression are also based on PCWG3, involving more complex data collection and derivation than RECIST 1.1. In this paper, we will share efficacy endpoints in prostate cancer, such as PSA response and time to PSA progression. We will explain the ADaM design and data flow, and how to ensure traceability and data dependency in derivation. We successfully implemented programming for these complex endpoints, enhancing the speed and quality of effective analysis through the development of macros | best response, adam dataset, randomization, duration of response, objective response rate, time-to-event endpoint, cdisc | source | 10 |
| 19 | ADaM Discussion Topics: PARQUAL, ADPL, Nadir | Sandra Minjoe | This paper and presentation will cover three topics that have been under varying levels of discussion within the CDISC ADaM team but are not part of the standard. First is the parameter-qualifier variable PARQUAL, which can be found in a couple Therapeutic Area User Guides (TAUGs), went out for public review as part of ADaMIG v1.2, but currently breaks BDS rules because it never made it into a final publication. Second is ADPL, a one-record-per-subject-per-participation dataset that might be useful for studies where subjects can enroll more than once or have multiple screening attempts, similar to the proposed SDTM DC domain. Third is Nadir variables, like Change from Nadir and Percent Change from Nadir, not currently allowed in a BDS structure. In each case, the paper and presentation will summari ze CDISC ADaM team discussions and give personal (not CDISC-authorized) recommendations of when and how to implement these concepts in order to meet analysis needs | adam dataset, change from baseline, cdisc | source | 7 |
| 20 | Adding the missing audit trail to R | Magnus Mengelbier | The R language is used more extensively across the Life Science industry for GxP workloads. The basic architecture of R makes it near impossible to add a generic audit trail method and mechanism. Different strategies have been developed to provide some level of auditing, from logging conventions to file system audit utilities, but each has its drawbacks and lessons learned. The ultimate goal is to provide an immutable audit trail compliant with ICH Good Clinical Practice, FDA 21 CFR Part 11 and EU Annex 11, regardless of the R environment. We consider different approaches to implement auditing functionality with R and how we can incorporate an audit trail functionality natively in R or with existing and available external tools and utilities that completely supports Life Science best practices, processes and standard procedures for analysis and reporting. We also briefly consi der how the same principles can be extended to other languages such as Python, SAS, Java, etc | audit trail, 21 cfr part 11, dataset, life sciences | source | 8 |
| 21 | Advancing Regulatory Intelligence with conversational and generative AI | Saurabh Das, Rohit Kadam, Rajasekhar Gadde, Niketan Panchal, Saroj Sah |
Staying up to date with regulatory requirements, especially in the field of life sciences, is extremely important. Regulatory authorities need to update regulations because of new guidelines, rapid innovation, changing global health scenarios, etc. to ensu re enhanced safety of patients. Life sciences organizations need to adhere to these changing regulations and take data-driven decisions. Traditionally, this involved a regulatory professional to frequently and manually search through multiple Regulatory sites (www.fda.gov, www.ema.europa.eu/en etc.) and monitor the changes in guidelines, updates etc. This process is extremely time-consuming and effort intensive. In order to overcome this challenge, a chatbot for regulatory services can provide an effective solution. This chatbot can simulate human -like conversations to allow for a user -friendly and easy -to-understand interface | life sciences, clinical trial | source | 7 |
| 22 | Advancing the Maturation of Standardized CRF Design | Sugumaran Muthuraj, Rubha Raghu |
Effective data collection using Case Report Forms (CRFs) can significantly impact the success of clinical trials. The evolution of standardized CRF design has been crucial in improving clinical trial outcomes. However, the SDTM standardization process is t ime-consuming due to challenges such as data inconsistencies, absent data, incomplete information, and sensitivity to case variations. Standardizing at the CRF level can mitigate the need for revisions and elevate overall data quality. Employing Artificial Intelligence (AI) to automate the field collection process in CRFs can serve as a fail-safe against inappropriate data collection, enhancing accuracy and reliability. AI, machine learning, and advanced data visualization techniques play an integral role in improving research and patient outcomes, including enhanced data quality, efficiency, and regulatory compliance | clinical trial, data cleaning, clinical research, artificial intelligence, data governance, data integrity, machine learning, medical history, crf, clinical data, likelihood function, accuracy, quality control, adverse events, cdisc | source | 9 |
| 23 | Adventures in Independent Consulting: Perspectives from Two Veteran Consultants Living the Dream | Joshua M. Horstman | While many statisticians and programmers are content in a traditional employment setting, others yearn for the freedom and flexibility that come with being an independent consultant. In this paper, two seasoned consultants share their experiences going independent. Topics include the advantages and disadvantages of independent consulting, getting started, finding work, operating your business, and what it takes to succeed. Whether you're thinking of declaring your own independence or just interested in hearing stories from the trenches, you're sure to gain a new perspective on this exciting adventure | clinical trial, credit card | source | 7 |
| 24 | Agile Sponsor Oversight of Statistical Programming Activities | Manuela Koska, Veronika Csom |
Agile sponsor oversight of a Contract Research Organization (CRO) for Statistical Programming involves a dynamic, collaborative, and flexible approach to monitoring and managing the CRO's work. Key characteristics of this method include constant communication, iterative processes, adaptive planning, proactive risk management, stakeholder engagement, and an unwavering focus on the quality of deliverables. In our discourse, we will explain in detail how oversight might be planned, conducted, and documented and highlight how the principles of agility can significantly contribute to its successful execution. A differentiation will be made between the concepts of sponsor oversight and vendor qualification, and it will be described how agile oversight aligns with risk-based principles, providing the flexibility to modify plans as necessary | clinical programming, clinical trial, statistical programmer, data science | source | 6 |
| 25 | Agile, Collaborative, Efficient (ACE): A New Perspective on Data Monitoring Committee Data Review Preparation | Ke Xiao | Data Monitoring Committee (DMC) has been part of clinical trials since 1960's. Nowadays, an increasing number of sponsors utilize DMC in various situations to monitor safety data, critical efficacy data, and ensure the integrity of study conduct. Given the different purposes of DMCs, there are diverse approaches to facilitate DMC data review. In this paper, we will discuss certain strategies and processes that Boehringer Ingelheim implement for DMC. To begin with, we will briefly describe the general purpose of a DMC as well as its roles and responsibilities in Boehringer, followed by an introduction to Boehringer's independent statistical analysis team (iSAT). The focus is to present three models that iSAT, as an independent team within sponsor, apply to efficiently and timely assist DMC data review. We will conclude by sharing experience/lessons learned from supporting DMCs in randomized non-pivotal and/or open label registrational studies to open further discussion | open label, clinical trial, phase ii, data science, data review, statistical programmer | source | 6 |
| 26 | AI and the Clinical Trial Validation Process - Paving a Rocky Road | Steve Ross, Ilan Carmeli |
The validation of outputs in a clinical research environment acts as a guarantor process, confirming the accuracy and validity of the trial results, the investment of doctors, patients, and caregivers in the efficacy and utility of the trial, and the reputation of the sponsor and/or CRO conducting the validation. Double programming has done this heavy lifting for decades. The increasing application of AI (ML and NLP) gives statisticians and programmers unprecedented opportunity to apply this technology wher ever validation takes place - during the development cycle to aid teams in getting to the right output sooner, and at the end of a study to check that tables in a package match what is expected for the deliverable | data cleaning, clinical trial, machine learning, mock shell, clinical research | source | 7 |
| 27 | Amazing Graph Series: Swimmer Plot - Visualizing the Patient Journey: Adverse Event Severity, Medications, and Primary Endpoint | Tracy Sherman, Aakar Shah |
Have you been tasked to map out the patient experience comprising of adverse events (AEs), related concomitant medication and efficacy endpoint data along the study duration? Have you ever used interactive feature of the graphs which allows you to communicate significantly more information with the stakeholders? This paper provides a vital analysis of clinical trial data, unveiling the nuanced interplay among AEs, concomitant medication, and primary endpoint. Introducing an advanced SAS method, the paper employs PROC SGPLOT with HIGHLOW and SCATTER statements to craft an interactive HTML swimmer plot, featuring data tip functionality. Additionally, the integration of information from three studies enhances the visualization of individual patient journeys |
adverse events, swimmer plot, concomitant medications, study day, proc sgplot, treatment-emergent adverse event, proc step, macro definition, adverse event severity, clinical trial | source | 16 |
| 28 | An approach to make Data Validation and Reporting tool using R Shiny for Clinica Data Validation | Madhavi Gundu, Vivek Jayesh Mandaliya |
Data validation in clinical trials plays a critical role in ensuring the integrity, reliability, and validity of the data collected during the study. Clinical trials are essential for evaluating the safety and efficacy of new drugs, medical devices and results of these trials can have significant implications for patient care, regulatory approvals, and public health. Traditionally, data validation has relied heavily on proprietary software such as SAS to generate reports, which may come with limitations in terms of flexibility and accessibility. With the advent of Open -source tools like RStudio and Python, we have developed a Data validation tool using R Shiny. The tool introduces a dynamic user-friendly interface with the option to select data points and logic blocks that a user can customize per the validation criteria and generate reports without any programming skills. Our tool empowers Data management teams to conduct efficient and accurate data validation | data cleaning, data validation rules, create table, dataset, clinical trial, excel | source | 10 |
| 29 | An introduction to Quarto: A Versatile Open-Source Tool for Data Reporting and Visualization | Joshua J. Cook | In the collaborative landscape of data analysis, a common frustration among analysts stems from the need to integrate and harmonize different programming languages within a team. Teams often comprise interdisciplinary researchers, each with their unique programming preferences and expertise, leading to complexities in project integration and continuity. The difficulty in compiling and executing data projects cohesively can hinder efficiency and impede the delivery of coherent, multi-faceted data insights. This challenge necessitates a solution that can bridge the gaps between varying coding languages and methodologies to streamline team collaboration and project completion. Addressing this issue, the point of this paper is to present Quarto as an innovative solution that can unify the diverse programming approaches within a team. Quarto stands out by offering extensive cross- language support, enabling the integration of code from multiple languages into a singular, dynamic report | data science, primary endpoint | source | 18 |
| 30 | An Introduction to the SAS Transpose Procedure and its Options | Timothy J. Harrington | PROC TRANSPOSE is a SAS procedure for arranging the contents of a dataset column from a vertical to a horizontal layout based on selected BY variables. This procedure is particularly useful for efficiently manipulating clinical trials data with a large bnumber of observations and groupings as is often found in laboratory analysis or vital signs data. The use of PROC TRANSPOSE is illustrated with examples showing different modes of arranging the data. Possible problems which can occur when using this procedure, and their solutions are also discussed |
proc transpose, proc step, by group processing, in=, missing value, laboratory data, out=, vital signs, sample, macro variable | source | 14 |
| 31 | Analyzing your SAS log with user defined rules using an app or macro. | Philip A. Mason | SAS provide some pretty basic help with logs that are produced, typically just linking to errors and warnings. Many people build log checkers to look for particular things of interest in their logs, which usually involves capturing the log and then running some SAS code against it. I made a way to define rules in JSON format which can be read by a SAS macro and used to look for things in a log. This means different rules can be used for different use cases. They can be used via a macro or via a web applicat ion I build. The web app can switch between rules, provides summaries, draws diagrams of the code, provides performance stats, and more. Hopefully this functionality might one day be built into SAS, but in the meantime it works well as an addition | sas log, macro variable, out=, macro definition, cpu time | source | 10 |
| 32 | Applications of Machine Learning and Artificial Intelligence in Real World Data in Personalized Medicine for Non-Small Cell Lung Cancer Patients | Sherrine Eid, Samiul Haque, S. Robert Collins |
Introduction: Lung cancer is the most commonly diagnosed cancer and leading cause of cancer death in 2018. Most cases are Non-Small Cell Lung Cancer (NSCLC). Machine learning methods (ML) and Real World Data (RWD) make personalized medicine a viable possibility to save patients' lives. We explored patient journeys and novel opportunities to save lives. Chronic Obstructive Pulmonary Disease (COPD) commonly persists during cancer treatment and requires interventions that could jeopardize the effectiveness of the treatment and their prognosis. Methods: This study included 1.2 Million NSCLC (ICD-10 C34.*) patients from 2019-2020 using Symphony Health Claims (Symphony Health Solutions, ICON Plc). Patient journeys using diagnoses were established with Path Analysis | likelihood function, gradient boosting, machine learning, logistic regression, proc hpforest, artificial intelligence, logistic distribution, regression analysis, correlation | source | 8 |
| 33 | Around the Data DOSE-y Doe, How Much Fun Can Your Data Can Be: Using DOSExx Variables within ADaM Data Sets | Inka Leprince, Richann J. Watson |
In the intricate dance of clinical trials that involve multiple treatment groups and varying dose levels, subjects pirouette through planned treatments - each step assigned with precision. Yet, in the realms of pediatric, oncology, and diabetic trials, the challenge arises when planned doses twirl in the delicate arms of weight-adjustments. How can data analysts choreograph the Analysis Data Model (ADaM) data sets to capture these nuanced doses? There is a yearning to continue with the normal dance routine of analyzing subjects based on their protocol-specified treatments, yet at times it is necessary to learn a new dance step, so as not to overlook the weight-adjusted doses the subjects actually received. The treatment variables TRTxxP/N in the Subject-Level Analysis Dataset (ADSL) and their partners TRTP/N in Basic Data Structure (BDS) and Occurrence Data Structure (OCCDS) are elegantly designed to ensure each treatment glides into its designated column in the summary tables | clinical trial, dose, treatment period, adverse events, adam dataset, clinical research, clinical data, data science, cdisc | source | 12 |
| 34 | Assurance in the Digital Age: Automating MD5 Verification for uploading data into a Cloud based Clinical Repository | Laura Elliott, Ben E. Bocchicchio |
Movement of data in and out of cloud-based repositories has become increasingly more common with large clinical trials spanning different cloud-based system over time. The movement of data between these systems could become compromised if not properly vetted. Verifying the integrity of data for clinical trials is of utmost importance, however, many clinical data repositories do not offer an easy way to confirm whether files migrated from one system to another remain the same. This paper discusses a process that creates a validation report comparing md5 checksums from source to destination checksums. The process, written in python, generates a CSV file of checksums from the source and a unique JOB file both of which are uploaded to the clinical repository via REST APIs. REST APIs then execute the unique job which runs a comparison SAS code. The code generate a md5 checksum dataset for files in the clinical repository destination and compares them with the local values found in the CSV |
data integrity, rest api, clinical trial, batch job, data cleaning, hashing function | source | 6 |
| 35 | Automated Harmonization: Unifying ADaM Generation and Define.xml through ADaM Specifications | Wei Shao, Xiaohan Zou |
In electronic submission packages, ADaM datasets and Define.xml stand as pivotal components. Ensuring consistency between these elements is critical. However, despite the importance of this, current methods still heavily depend on manual checks. To address this challenge, we introduce an innovative automated approach driven by ADaM specifications. Our solution involves a suite of SAS macros engineered to streamline the translation from ADaM specification to both ADaM datasets and Define.xml. These macros orchestrate a seamless automation process, facilitating the generation of ADaM datasets while concurrently fortifying consistency between ADaM datasets and Define.xml. The automated processes include format creation, core variables addition, variable attributes generation, dynamic length adjustment based on actual values, and automatic ADaM specification updates from actual data | adam dataset, length statement, character variable, macro variable, proc step, macro call, proc sql, cdisc | source | 10 |
| 36 | Automating the annotation of TLF mocks Using Generative AI | Vidya Gopal | In this paper we will be discussing the use of Generative AI to automate the mapping and annotation of ADaM variables to TLF mocks to increase the efficiency, quality, and accuracy of TLFs. The TLF mock provides a specification for how the TLF should be created from the ADaM dataset. In a study, annotating the mock is crucial to provide quality and traceability. Annotating a mock is time and labor intensive due to the volume of TLFs and ADaM variables. This proof-of-concept proposal would entail exploring the use of Generative AI prompts to match the variable name given in the ADaM specification document to the general name given in the TLF mock shell. For instance, if the ADSL specification includes a variable named AGECAT, this idea would aim to map this variable with the "Age Group" value in a demographic table template. This can be done through techniques such as similarity analysis of ADaM dataset metadata | adam dataset, mock shell, cdisc | source | 6 |
| 37 | Automating third party data transfer through digitized Electronic DTA Management | Vandita Tripathi, Manas Saha |
CDM is a pivotal process in clinical trial, and not all data is directly captured into case report forms (CRFs), a large portion is also collected from external sources like third-party vendors, labs, wearables, devices, and external systems. This type of data is called "non-CRF data". Maintaining Data integrity and quality of the non-CRF data has a critical influence on clinical data management process and study success. To ingest non-CRF data from external third parties, Data Transfer Agreements (DTA) are signed between sponsor and vendor organizations. DTA defines the structure, frequency / timelines, and data definitions which enables transfer of non-CRF data from vendor to the study database. Often the DTA authoring, and review process takes months which is a significant challenge in the industry. Non CRF data received from vendors needs to be reconciled with CRF data which often takes lot of time due to associated quality and standard related issues with the non-CRF data | clinical trial, data governance, data cleaning, data standard, proc freq | source | 6 |
| 38 | Automation and integration of data visualization using R ESQUISSE & R SHINY | Vijayakumar Radhakrishnan, Nithiyanandhan Ananthakrishnan |
In this paper, we will be discussing how R ESQUISEE can be instrumental in implementing automation and MLP for data visualization. R Esquisse is an R program that provides a user-friendly interface for creating data visualizations with ggplot2. This package is especially useful for stakeholders that require quick interactive analysis and insights from varied data sources. With R Esquisse, you can quickly drag and drop variables from your data onto a plot canvas, customize plot features like titles, axis labels, and colors, and generate ggplot2 charts with a few clicks. R Esquisse's UI is comparable to Tableau, a prominent data visualization software, making it simple for Tableau users to adapt to R Esquisse. It also has advanced features like multi-language compatibility and an in-browser application, which further enhances the user experience | data visualization, big data | source | 6 |
| 39 | Automation of Report Generation Beyond Macro | Hong Qi, Mary N. Varughese |
SAS macros have been widely used to improve efficiency and standardization of data analysis and reporting in clinical trials for regulatory submission. These macros designed for repetitive tasks and streamlining the process of report generation are created at different levels - global, therapeutic area- and protocol-specific, to meet the requirements of various reporting needs. However, relying solely on macros may be burdensome, especially in late-stage oncology clinical studies where numerous data analysis reports are generated during a study. Within each deliverable, summary and analysis reports often exhibits common characteristics such as tumor type, biomarker, subgroups, and more. Traditionally, a separate call-macro program is created for each report, requiring manual adjustments to address different requests. To overcome these challenges and to enhance automation, an approach has been developed using standardized call-macro programs in conjunction with a Level-II SAS program | clinical trial, macro variable, data science, macro parameter, macro call, macro definition | source | 6 |
| 40 | AutoSDTM Design and Implementation With SAS Macros | Chengxin Li | CDISC standards have been widely deployed in the pharmaceutical industry for the past decade. The main effort has focused around complying with CDISC standards. While complying with CDISC standards, the focus should shift toward developing a reusable automated solution. The metadata of raw data varies across companies . However, the metadata patterns are limited and the metadata of targeted SDTM are rigid defined in SDTMIG. This provides the feasibility of SDTM mapping automated. This paper introduces a universal autoSDTM solution implemented with SAS macros. Design goals 1. No dependence on data management settings thus a universal solution; 2. Besides current SDTMIG 3.3 compliance, output with submission ready; 3. Process transparent to facilitate review; 4. Following SDTM programmer's practices thus with low transition cost; 5. User friendly; 6. Most importantly, high efficiency while ensuring high quality. Implementations 1. One SAS macro per domain; 2 | macro parameter, macro definition, raw data, sdtm domain, macro call, pharmaceutical industry, cdisc | source | 10 |
| 41 | AutoVis Oncology Presenter: Automated Python-Driven Statistical Analysis and Visualizations for Powerful Presentations | Indraneel Narisetty | In late-phase or first-in-human clinical studies, understanding clinical data is vital for informed decision making, such as selecting the appropriate drug dose and evaluating its efficacy and safety. The traditional process of converting ADaM datasets into TLFs (tables, listings, and figures) and integrating them into clinical PowerPoint presentations has historically been a time-consuming task. Medical monitors and clinical teams create these presentations to conclude on dose selection, escalation, and drug effectiveness. Addressing this need, we've introduced "AutoVis Oncology Presenter," an innovative Python-based tool designed to streamline the transformation of Oncology clinical trials data into clear, impactful PowerPoint presentations. It's particularly adept at handling key ADaM datasets like ADTTE and ADRS, which are crucial for assessing treatment effectiveness. This paper will demonstrate how to build this tool, complete with Python code and practical examples | clinical trial, proc means, data science, proc freq, dataset, cdisc | source | 12 |
| 42 | Bayesian Additive Regression Trees for Counterfactual Prediction and Estimation | Michael Lamm | Bayesian additive regression trees (BART) are a flexible and easy-to-use class of nonparametric models that are consistently cited as one of the best-performing tools for causal analysis of real-world data. Applications where BART models have shown great success include estimating causal effects, analyzing subgroups, and assisting in the incorporation of historical data. Properties of BART models that make them well suited for these tasks are their ability to capture potentially complex interactions without explicitly prespecifying and modeling them, the good generalization of the defaul t model, and their ability to take a Bayesian approach to uncertainty quantification. This paper discusses the implementation of BART models in SAS Visual Statistics software by using the BART procedure, and it demonstrates how you can use this procedure to estimate counterfactual outcomes and causal effects that are defined in a counterfactual framework | sample, potential outcomes, proc step, training set, regression analysis, treatment effect, causal inference, gradient boosting, proc hpforest, credible interval, hyperparameter tuning, response variable | source | 10 |
| 43 | Bayesian Methods in Survival Analysis: Enhancing Insights in Clinical Research | Vadym Kalinichenko | Survival analysis has long been a cornerstone in understanding time -to-event data. However, traditional survival analysis often relies on frequentist statistics, which can be limited in handling complex scenarios and incorporating prior knowledge. Hence, i t is a prudent decision to integrate Bayesian methods, a transformative approach that offers a fresh perspective to survival analysis. This paper peers into the application of Bayesian methods in survival analysis, presenting a nuanced perspective that enhances insights in clinical research. Bayesian approaches provide a robust framework for handling complex survival data, incorporating prior knowledge, and facilitating more informed decision- making. This paper demonstrates how Bayesian approaches increase the understanding of survival outcomes, contributing significantly to the advancement of clinical research | survival analysis, bayesian statistics, prior probability, hazard ratio, likelihood function, posterior probability, sample, proc phreg, clinical trial, null hypothesis, hazard function, regression analysis, proc step, clinical research, confidence interval, standard deviation, probability distribution, maximum likelihood estimation, estimate, sensitivity analysis, survival function, kaplan-meier estimator | source | 11 |
| 44 | Bayesian Models with the Power Prior Using PROC BGLIMM | Yi Gong, Fang K. Chen |
The power prior, a general class of priors that are used in Bayesian analysis, provides a practical and dynamic approach to translate data information into distributional information about the model parameters. Since its introduction, the power prior has played an increas- ingly prominent role in many disciplines. As popular as this prior has become, a software problem persists: implementation of the power prior can be difficult using Bayesian software packages and often relies on programming solutions that are problem-specific, making it hard to generalize. In this paper, we introduce new features in the SAS BGLIMM procedure that will enable you to fit the power prior to many models (repeated measurements models, random-effects models, missing data problems, etc.) with the simplest setup |
posterior probability, proc step, likelihood function, proc mixed, covariance, prior probability, latent variable, response variable, sample, binomial distribution, regression analysis, missing value, normal distribution, proc freq | source | 15 |
| 45 | Benefits, Challenges, and Opportunities with Open-source Software (OSS) Integration | Kirk Paul Lafler, Ryan Paul Lafler, Joshua J. Cook, Stephen B. Sloan |
The open-source world is alive, well and growing in popularity. This paper highlights the many benefits found with open-source software (OSS) including its flexibility, agility, talent attraction, and the collaborative power of community; the trends show that open-source is ubiquitous penetrating many critical technologies we depend on, where more technology companies recognize the importance of the open-source community leading to more initiatives and sponsorships that support open-source creators. The challenges of open source include compatibility vulnerability issues, security limitations, intellectual property issues, warranty issues, and inconsistent developer practices. The opportunities coming out of the open-source community include cloud architecture, open standards, and the collaborative nature of community | data science, proc sql | source | 7 |
| 46 | Best Function Ever: PROC FCMP | Michael Stout | Base SAS provides a wealth of character and numeric functions. User defined functions can be created with PROC FCMP (SAS Function Compiler). PROC FCMP is a powerful tool that can invoke SAS procedures and the output delivery system and then return a single value to the calling program. This paper will show how to write and use user defined functions created using the speci al SAS function RUN_MACRO. Now you can create the best functions ever and deploy them in your SAS environment |
proc fcmp, proc step, sas function | source | 4 |
| 47 | BIMO Brilliance: Your Path to Compliance Resilience | Jai Deep Mittapalli, Girish Kankipati |
The FDA's Bioresearch Monitoring (BIMO) program is pivotal in upholding the integrity of data generated in clinical trials and post-marketing surveillance of drug products, safeguarding the rights and safety of human subjects. In this paper, we delve into the critical aspects of BIMO submissions and the technical conformance guide. We aim to elucidate the key components the FDA expects in a BIMO package, the significance of the CLINSITE data set, and the four essential listings that must be part of the submission. Furthermore, we explore the structural and formatting requirements of a BIMO package and emphasize the compliance standards that must be adhered to. Drawing from our recent BIMO submission experience, we offer insights to aid in understanding and preparing for future BIMO submissions | clinical trial, clinical programming | source | 5 |
| 48 | Bridging AI and Clinical Research: A New Era of Data Management with ChatGPT | Illia Skliar | The integration of ChatGPT, an advanced language model, into the realm of clinical data management marks a significant evolution in the methodology of statistical programming within clinical trials. In this session we will delve into the fundamental aspects of ChatGPT, explaining its mechanics and potential as a transformative tool in generation sample data. Specifically, we'll explore the way for ChatGPT to automatically create SAS data sets containing raw data using study protocols, statistical analysis plans, and Case Report Forms (CRFs). Moving on, the paper will describe an innovative approach in automating the production of data sets with raw data for training purposes, tailored for specific needs of clinical trials. The discussion emphasizes how ChatGPT can improve data readiness and facilitate a more efficient trial initiation process | clinical trial, sample, data governance, macro variable, medical history, raw data, data science, clinical research, clinical data, dataset, machine learning, artificial intelligence, txt, vital signs | source | 12 |
| 49 | Build up Your Own ChatGPT Environment with Azure OpenAI Platform | Bill Zhang, Jun Yang |
ChatGPT from OpenAI is a state-of-the-art chatbot technology that uses natural language processing to understand and respond to user queries. With ChatGPT, you can do your daily work more efficiently and effectively. This article provides a step-by-step guide on how to configure and extend a customized, secure ChatGPT environment with Azure OpenAI platform for your organization. The article starts with an introduction to the topic and explains why it is important to configure and deploy a customized ChatGPT environment for pharmaceutical industry. It then provides some architecture information on internal ChatGPT environment with Azure OpenAI platform & Azure AI search services, explaining what they are, how they work together | sample, access control | source | 10 |
| 50 | Build Your Own PDF Generator: A Practical Demonstration of Free and Open-Source Tools | James Austrow | The PDF is one of the most ubiquitous file formats and can be read on nearly every computing platform. So how, in the year 2024, can it still be so inconvenient to perform basic editing tasks such as concatenating and merging files, inserting page numbers, and creating bookmarks? These features are often locked behind paid licenses in proprietary software or require that the documents be uploaded to a web server, the latter of which poses unacceptable security risks to many would-be users. In fact, the PDF is a public standard and there exist free, open-source libraries that make it easy to build in-house solutions for these and many other common use cases. In this paper, we demonstrate how to use Python to automatically assemble multiple PDF documents into a unified, polished deliverable. No more than a basic familiarity with Python should be needed to follow along; we thoroughly explain every step from start to finish | ods pdf, macro definition | source | 19 |
| 51 | Building Complex Graphics from Simple Plot Types | Dan Heath | There are many types of graphics, such as survival plots, adverse event plots, and forest plots, used to report clinical data. However, many of these graphics might not be available as a simple request. Even if they are available, there can be many variations on these graphics, and the reporting requirements can be different from one company to another. In this hands-on workshop, I want to show you how to construct these types of graphics by combining simple plot types, sometimes in very creative ways. We will discuss how to think about a graph as the sum of its parts. This will empower you to create these types of graphics in whatever form you need | waterfall chart, forest plot, scatter plot, bar chart, kaplan-meier plot, swimmer plot, complete response, procedures | source | 17 |
| 52 | CDISC Therapeutic Area User Guides and ADaM Standards Guidance | Karin LaPann | One of the frequently overlooked yet immensely valuable resources for implementing standards are the CDISC Therapeutic Area User Guides (TAUGs). Presently the CDISC website hosts 48 of these guides, 26 of which incorporate ADaM sections. These guides are created by groups of CDISC standards volunteers across the industry and include medical professionals and researchers with experience in the respective disease areas. The first few years of development, these TAUGs concentrated on the collection of the data and the implementation of the SDTM to contain it. In 2014 the first TAUG with an analysis section using ADaM was published. Many TAUGs are developed with additional implementation of the analysis datasets, with ADaM compliant examples. This provides a utility to the programming community to illustrate how the SDTM datasets are further arranged for analysis. The latest initiative has been to expand these TAUGs through grants by organizations representing various diseases | sdtm domain, sample, adam dataset, cdisc | source | 6 |
| 53 | Challenges and Considerations When Building e-Submission SDTM Data Packages | Wei Duan | When it comes to e-Submission SDTM package preparation, there are always waves of hills to climb. With helpers like Pinnacle 21 Enterprise (P21 E), in close collaboration with internal Standard team and Data Management team, programmers can keep up to speed with CDISC, TAUG, and standard requirements from regulatory authorities, and ensure compliance of Standard Data Tabulation Model (SDTM), as the root of data for reviewers, and a solid groundwork and pillars for subsequent ADaM and TLF workflows. In a recent BLA submission of vaccine studies, SDTM package preparation has not been a simple standard in-and-out process. As the devil is always in the details, in this paper we will go through several noteworthy challenges and considerations lying behind the scenes, along our journey building the e-Submission SDTM packages | data cleaning, sdtm domain, sdtm programming, clinical trial, data governance, cdisc | source | 8 |
| 54 | Combine PDFs in Submission-ready Format Quick and Easy | Robin Wu, Lili Li, Steven Huang |
Portable Document Format (PDF) is an essential component in the regulatory submission package (U. S. Food and Drug Administration, 2013; International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use, 2017). The conventional approaches of combining PDFs include using a SAS macro or utilizing a third-party stand-alone tool. Both approaches are usually time- consuming, unstable, and technically challenging in real practices. In the meantime, however, very limited research and utilities have been shared in the SAS community on how to combine PDFs properly and efficiently in a submission-ready format. This paper will address the gap here by introducing a novel approach that implements both a SAS macro and two third-party utilities (a Visual Basic application and a Java application) to make the PDF combination quick, easy, and submission-ready | macro definition, cpu time, sample | source | 6 |
| 55 | Combining Functions and the POLYGON Plot to Create Unavailable Graphs Including Sankey and Sunburst Charts | Jeffrey P. Meyers | SAS currently has an expansive array of plot types available within the SG procedures and Graph Template Language, but there are still charts that are too complex to create using conventional means. Fortunately, SAS has a powerful trick up its sleeve to allow the ambitious programmer to design these charts: the polygon plot. A polygon plot allows the programmer to use geometric, trigonometric, and other equations to manually produce x and y coordinates that form the perimeter around a shape with the option to fill in the shape with color. This paper will walk through utilizing the polygon plot to create circular, twisting, and other complex graphs including as the Sankey diagram and sunburst chart | bar chart, do loop, proc freq | source | 20 |
| 56 | Comparing SAS and R Approaches in Creating Multicell Dot Plots in Statistical Programming | Yi Guo | In clinical studies, comparisons between groups are very common, and researchers are typically interested in significant results or differences. Instead of laboriously examining statistical tables row by row, multicell dot plots that share the same y-axis can provide a more intuitive and efficient way for researchers to pinpoint the results of interest. Both SAS and R can create high-quality dot plots. In this paper, we will explain and compare the risk difference (RD) within the system organ class (SOC) for two treatment groups using SAS GTL versus R ggplot2. Furthermore, we will discuss the challenges when using these different programming languages, compare the syntax and functionality of SAS and R, and summarize which method is more efficient in different aspects. In addition, the paper will explore the SAS and R code in an application of dot plots in a real-world evidence (RWE) study | propensity score matching, propensity score, system organ class, data visualization, adverse events, proc freq | source | 14 |
| 57 | Comparison of Techniques in Merging Longitudinal Datasets with Errors on Date Variable: Fuzzy Matching versus Clustering Analysis | Huitong Niu, Yan Wang |
This paper examines effective techniques for merging longitudinal data sets with key variable inaccuracies, focusing on date errors. Traditional SAS methods, like the DATA STEP MERGE or PROC SQL JOIN, require exact matches on key variables, which is challenging in data sets containing errors. Our paper compares fuzzy matching and clustering analysis within SAS, assessing their effectiveness in reconciling data sets with inconsistencies in date variables. We simulate a longitudinal data set of approximately 2,000 observations, representing about 500 patients with repeated measurements. The data set is used to simulate two data sets including normally (or uniformly) distributed errors on date, manually introduced errors (e.g., typing "12" as "21"), and missing date information (e.g., entering "06/23" instead of "12/06/2023"). For each scenario, we use fuzzy matching and clustering analysis to merge two data sets, evaluating the accuracy of each technique |
date variable, cluster analysis, longitudinal data, proc step, proc fastclus, proc sql | source | 11 |
| 58 | Complex Custom Clinical Graphs Step by Step with SAS ODS Statistical Graphics | Richann J. Watson, Joshua M. Horstman |
chi-square test, bar chart, macro variable, proc sgplot, sas base, output delivery system, graph template language | source | 44 | |
| 59 | Comprehensive Evaluation of Large Language Models (LLMs) Such as ChatGPT in Biostatistics and Statistical Programming | Songgu Xie, Michael Pannucci, Weiming Du, Huibo Xu, Toshio Kimura |
Generative artificial intelligence using large language models (LLMs) such as ChatGPT is an emerging trend. However, discussions using LLMs in biostatistics and statistical programming have been somewhat limited. This paper provides a comprehensive evaluation of major LLMs (ChatGPT, Bing AI, Google BARD, Anthropic Claude 2) in their utility within biostatistics and statistical programming (SAS and R). We tested major LLMs across several challenges: 1) Conceptual Knowledge, 2) Code Generation, 3) Error Catching/Correcting, 4) Code Explanation, and 5) Programming Language Translation. Within each challenge, we asked easy, medium and advanced difficulty level questions related to 3 topics: Data, Statistical Analysis and Display Generation. After providing the same prompts to each LLM, responses were captured and evaluated. For some prompts, LLMs provided incorrect responses, also known as "hallucinations" | data science, statistical programmer, clinical programming | source | 9 |
| 60 | Converting FHIR to CDASH using SAS | Pritesh Desai, Mary Liang |
With the growing diversity of standards for collecting and presenting Real World Evidence (RWE), there is an escalating demand for the conversion of these standards into more actionable datasets. This paper demonstrates the transformation from FHIR (Fast Healthcare Interoperability Resource) to CDASH using various methods within SAS Viya. The outlined methods are easily adaptable to other standards or datasets initially presented in JSON format. Moreover, recognizing the need for accessible processes, we will highlight the creation of low/no code procedures to enhance access to these updated datasets, including the transformation of conversion work into SAS Viya Custom Steps | sas viya, clinical trial, proc step, clinical data, cdisc | source | 5 |
| 61 | Create a Shift Summary of Laboratory Values in CTCAE Grade to the Worst Grade Abnormal Value using R and SASSY System | Vicky Yuan | A shift summary of laboratory values in CTCAE grade to the worst grade abnormal value is often required for most laboratory data analysis and submission. The purpose of CTCAE grade shift table is to present how the results vary from the baseline to post-base visits in the study. This paper will illustrate how to report a shift table using R and packages from the SASSY system. It will start from an example and explain the anatomy, then a step-wise explanation of how to report the table in a PDF file. The example is interesting because the columns and "internal" footnotes change on every page | shift table, ctcae grade, proc step | source | 18 |
| 62 | Creating a Culture of Engagement: "Role of a Manager" | Monali Khanna | Work could be just work, or it could additionally bring a sense of purpose and satisfaction. What could contribute most to your team being high-performing and happier at work? Could you do something to make your team feel genuinely cared for, appreciated, and more productive at work? A high-performing workforce is essential for survival and growth, and a highly engaged workforce is more likely to go the extra mile to contribute to the organization's mission and goals. We all who work in the life sciences and healthcare industry have a unique opportunity where our work has a strong effect on many lives and that, when recognized, may result in an engaging and fulfilling career. As leaders, we can engage our teams more, so they feel empowered, contribute to creating essential goals and objectives, and feel good about it. This presentation will focus on what employee engagement is, why employee engagement is critical, what employees need to feel engaged, and how to engage employees | source | 3 | |
| 63 | Creating Adverse Event Tables using R and SASSY System | Vicky Yuan | Currently, pharmaceutical industry is focusing on adopting R language for the creation of submission packages and TLFs generation. SAS programmers who come to R are often frustrated because there are many reporting packages in R. Selecting a right package for programmers became more and more important. If a certain R package can provide functions, which have an ability to create data libraries, traceable log, format catalogs and easy way to reports as DOCX, rich-text files like what SAS did. sassy system seems meet all the criteria. This paper will provide on-hand overview of generating adverse event table by using sassy system. The R product used in this paper is R sassy system version 1.2.3 running on RStudio environment | adverse events, system organ class | source | 11 |
| 64 | Creating Custom Excel Spreadsheets with Built-in Autofilters Using SAS Output Delivery System (ODS) | Kirk Paul Lafler | Spreadsheets have become the most popular and successful data tool ever conceived. Current estimates show that there are more than 750 million EXCEL users worldwide. A spreadsheet's simplicity and ease of use are two reasons for the growth and widespread use of EXCEL around the globe. Additional value-added features have also helped to expand the spreadsheet usefulness among a growing number of users including its collaborative capabilities, being customizable, ability to manipulate data, application of data visualization techniques, mobile device usage, automation of repetitive tasks, integration with other software, data analysis, and filtering capabilities using autofilters. This last value-added feature, filtering with autofilters, is the theme for this paper |
excel, proc report, output delivery system | source | 6 |
| 65 | Creating Dated Archives Automatically with SAS | Derek P. Morgan | When creating patient profiles, it can be useful for clinical scientists to compare current data with previous data in real time without having to request those data from an Information Technology (IT) source. This is a method for using SAS to perform the archiving via a daily scheduled job. The primary advantage of SAS over an operating script is its date handling ability, removing many difficult calculations in favor of intervals and functions. This paper details an application that creates dated archive folders and copies SAS data sets into those dated archives, with automated aging and deletion of old data and folders. The application allows clinical scientists to customize their archive frequency (within certain limits.) It also keeps storage requirements to a minimum as defined by IT. This replaced a manual process that required study programmers to create the archives, eliminating the possibility of missed or incorrectly dated archives | macro variable, proc freq, proc step, sysdate9, proc sql, macro loop, date variable, macro definition, %if-%then-%else, excel, proc copy, dataset | source | 19 |
| 66 | Cultivating Success with Non-standard Investigator-sponsored Trial Data for FDA Submissions | Jai Deep Mittapalli, Jinit Mistry, Venkatesulu Salla |
From the moment of extraction of raw data all the way through creating analysis reports, a study programming team goes through multiple steps such as checking the quality of raw data, mapping, and programming SDTM and ADaM data sets, etc. A study lead programmer is responsible for all these s teps, while managing the programming team's assignments and activities, understanding the requirements given by Biostatisticians, working with cross-functional teams, finalizing the scope of the analysis, managing external data, data integration, and so on. Things become even more complex when study deliverables are submitted to regulatory agencies | raw data, adverse events, sdtm domain, sdtm programming, regulatory submission, data cleaning, laboratory data, protocol deviations, data science, missing value, edc, cdisc | source | 9 |
| 67 | Data Harmony Revolution: Rocking Trials with Clinical Data Literacy | Melanie Hullings, Emily Murphy, Andrew Burd, Helena Belloff |
Clinical DataAnalyticsandProgramming(CDAP) iscenteredaroundthecollection, monitoring, reporting,andanalysisof clinical trial data. Our team'sobjectiveistoimprovetheefficiencyof clinical trialsbyproducinghighquality, reliable, andstatisticallyrobust clinical datasets. Inorder toaccomplishthis,analystsontheteammust haveanunderstandingof all aspectsof technical frameworksandclinical trialoperations, includingmedical andsafetymonitoring. At thesametime, clinical trialsrequirealarge,multi-disciplinaryteamacrossmanydifferent highly-specializedclinical, operational, andtechnical roles.Becauseof thevariedbackgrounds, it ishardfor everyonetounderstandwhat theother teamsaredoing,interdependencies, anddownstreameffectsof decisions.Inaneffort tobridgeanygapsinunderstandingacrosstheinterdisciplinaryteams, CDAPrana12-weekinternal internshipprogramopentoall employees | clinical trial, data science | source | 5 |
| 68 | Data Literacy 101: Understanding Data and Extracting Insights | Kirk Paul Lafler | Data is all around us and growing at extraordinary rates, so anyone working with , or planning to work with, data should acquire a solid foundation in this essential skill. In this presentation, join Kirk Paul Lafler as he focuses on the fundamentals of data literacy, how to derive insights from data, and how data can help with decision -making. Lafler uses SAS software to explore data with graphs and describes data with statistics to help anyone improve their understanding of data and to enable more informed data-driven decisions. Attendees learn about the different types of data - nominal, ordinal, interval, and ratio; how to assess the quality of data; explore data with scatterplots, line charts, and box plots; and use selected statistical methods to describe data. By the end of this presentation, you'll have a much better understanding of why data literacy is so important in the 21st century, as well as learning how to identify the correct types of data for you to use | dataset, data science | source | 9 |
| 69 | Dealing with Missing Data: Practical Implementation in SAS and R | Isabella Y. Wang, Lauren George, Jin Xie |
Missing data is a prevalent challenge encountered in Phase III clinical trials. The appropriate handling of missing data becomes crucial and depends on the specific estimand under consideration. In this paper, we will delve into realistic examples of four popular missing data imputation methods and accompany with relevant SAS and R code. This paper aims to equip clinical trial professionals with practical tools to address missing data effectively and bridge the gap between theoretical understanding and actual application in handling missing data | imputation, missing value, mcmc, proc step, markov chain, monte carlo, treatment policy strategy, clinical trial, intercurrent event, multiple imputation, while-on-treatment | source | 20 |
| 70 | Deep Dive into the BIMO (Bioresearch Monitoring) Package Submission | Mathura P. Ramanathan, Nancy Brucken |
As a part of the review process for regulatory submissions to the Food and Drug Administration (FDA) by both pharmaceutical companies and CROs, the FDA carries out site-level inspections to ensure the integrity of the data submitted, and to verify that the rights, health, and welfare of those who participated in the studies were protected. To efficiently audit the sites, the FDA has established the Bioresearch Monitoring (BIMO) Program for the studies being submitted for their review. Currently, the BIMO package includes three required components: 1. Clinical Study-Level Information, 2. Subject -Level Data Line Listings by clinical site, and 3. Summary -Level Clinical Site (CLINSITE) Dataset, and an optional but important document, called the BIMO Data Reviewer's Guide (BDRG). The scope and format of these components are governed by the BIMO Technical Conformance Guide (TCG) and BDRG template | clinical trial, efficacy population, protocol deviations, treatment arm, adverse events, concomitant medications | source | 8 |
| 71 | Define-XML Conversion: A General Approach on Content Extraction Using Python | Danfeng Fu, Dickson Wanjau, Ben Gao |
Define-XML is a critical component of clinical trial data packages submitted to regulatory agencies as part of a new drug/ biologics application. It provides a standardized way to exchange the metadata for SDTM and ADaM datasets, which are used for analysis and reporting. The rich information embedded in Define-XML is an ideal data source for automating consistency checks against study datasets and documents, e.g. ADRG. SAS is a natural choice for this work due to its widespread use in the pharmaceutical industry, but there are challenges. First, the information needs to be extracted and converted into SAS datasets, all of which are challenging, owing to XML language's natural complexity with nested elements and namespaces. The process is also cumbersome since one needs to create XML map files for parsing the XML structure. Encoding would be another issue to tackle, especially when the Define-XML contains non-ASCII characters | clinical trial, excel, clinical data, dataset, adam dataset, pharmaceutical industry, statistical programmer, cdisc | source | 15 |
| 72 | Demystifying Incidence Rates: A Step-by-Step Guide to Adverse Event Analysis for Novice Programmers | Yuting Peng, Ruohan Wang |
This paper delves into the intricacies of Adverse Event (AE) reporting within the realm of clinical trials, focusing on three distinct methods of treatment-emergent AE incidence rate computation: Incidence Rate (IR), Event Incidence Rate Adjusted by Patient-Years (EIR), and Exposure-adjusted Incidence Rate (EAIR). While Incidence Rate (IR) and Event Incidence Rate (EIR) have traditionally found favor in clinical trial assessments, this study is prompted by a recent information request from the FDA during a submission of a Supplemental Biologics License Application (sBLA), advocating the adoption of Exposure-adjusted Incidence Rate (EAIR) for summarizing AE data. This paper aims to provide a comprehensive understanding of each method by delineating their definitions, comparing their nuances, and contrasting their applications | adverse events, exposure-adjusted incidence rate, incidence, proc step, clinical trial, system organ class, proc freq, proc sql, create table, safety population, treatment-emergent adverse event, analysis set, trial elements | source | 10 |
| 73 | Design Considerations for ADaM Protocol Deviations Dataset in Vaccine Studies | Rashmi Gundaralahalli Ramesh, Jeffrey Lavenberg |
Per-protocol analysis of immunogenicity data in vaccine trials is essential for evaluating efficacy. The ADaM dataset for protocol deviations (ADPDEV) is critical in excluding samples with clinically important protocol deviations from the analysis. However , challenges arise in determining the impact of deviations in studies with multiple doses and concomitant vaccinations, which require different exclusion rules. For instance, the same deviation term may lead to participant level exclusions or visit level exclusions. If there are multiple concomitant vaccinations, then each vaccination may follow a different rule to qualify for exclusion at each visit. This paper focuses on creating ADPDEV dataset in vaccine studies, specifically addressing studies with multiple doses and/or concomitant vaccinations. We present a systematic approach for determining sample exclusions based on the nature and timing of prot ocol deviations | protocol deviations, sample, adam dataset, clinical trial, cdisc | source | 12 |
| 74 | Developing Web Apps in SAS Visual Analytics | Jim Box, Samiul Haque |
SAS Viya provides capabilities to develop web applications that let you use HTML pages to provide inputs to programs that use SAS, R, and/or Python to create, display, and share analysis results across your organization. The GUI uses standard HTML programming to collect the inputs and the system leverages the existing scalability and security of your cloud environment | randomization, bar chart, macro variable | source | 9 |
| 75 | Effectively Manage the Programming Team Using Microsoft Teams | Jeff Xia, Simiao Ye |
Microsoft Teams is a robust platform that serves as both a messaging application a tool for real-time collaboration, meetings, and effortless file sharing. In this paper, we demonstrate how Microsoft Teams can be utilized to efficiently manage a programming team, promote knowledge sharing, and streamline the onboarding process for new team members. The topics covered include: Knowledgebase sharing for tips, best practices, and know-how Effective communication via announcements for important messages * Seamless file sharing By leveraging Microsoft Teams, programming teams can optimize productivity, enhance communication, and foster a collaborative culture | database lock, sample | source | 7 |
| 76 | Efficient Repetitive Task Handling in SAS Programming Through Macro Loops | Chary Akmyradov | This paper delves into the optimization of repetitive tasks in SAS programming, a common challenge faced by data analysts and programmers. The primary focus is on harnessing the power of SAS macro programming techniques, specifically through the implementation of do loops within macros. Initially, the paper introduces the basics of SAS macros, outlining their significance in automating repetitive sequences of code, and providing a foundational understanding of macro variables and syntax. The discussion then progresses to the implementation of simple do loops within macros, highlighting their practicality in routine data manipulation tasks. Through a series of practical examples and use-case scenarios, the paper demonstrates the effectiveness of these loops in real-world applications | macro function, macro definition, macro variable, macro loop, do loop, proc sql, proc step, conditional do loop, data science, sas programming | source | 7 |
| 77 | Elevate Your Game: Leveling Up SDTM Validation with the Magic of Data Managers | Julie Ann Hood, Jen Manzi |
Selecting players armed with unique abilities to collaborate with is the key to crafting an unbeatable strategy while on the field, navigating a quest, or in the office. In the realm of standardized clinical trial data, not only can this break down departmental siloes, but it can also enhance the quality of study data, leading to the availability of more effective, efficient treatments earlier. Historically, due to the timing and content of SDTM validation, many organizations enlisted SDTM programmers with unraveling issues in these reports. However, there are some rules that require tracing SDTM data back to raw data or data collection to optimize decision-making. Seamlessly integrating data managers into this process can help slash time to resolution of these types of issues as well as boost overall data quality. This poster will provide important considerations and guidance on weaving data managers into the SDTM validation process | data cleaning, raw data, adverse events, sdtm programmer, data governance, data audit, data validation rules, clinical trial, cdisc | source | 5 |
| 78 | Embracing Diversity in Statistical Computing Environments: A Multi-Language Approach | Amit Javkhedkar, Sridhar Vijendra |
In the transition to Statistical Computing Environments (SCEs), organizations face the challenge of supporting multiple programming languages, including SAS, R, Python, and Julia. Managing these many programming languages, their versions, libraries/packages, and IDEs is challenging. This paper explores possible ways to standardize and deploy multiple versions of various programming languages within a single platform. It delves into the advantages, limitations, and potential strategies to enhance the experience for the key users in statistical programming teams. Specifically, we examine whether there exists a straightforward solution to this complex task, ultimately aiming to provide insights into optimizing programming and application development in open-source languages for statistical programmers | clinical trial, data science, clinical research, access control, clinical programming, data cleaning, audit trail | source | 6 |
| 79 | Enhanced Spider Plot in Oncology | Ilya Krivelevich, Cixin He, Binbin Zhang-Wiener, Wenyin Lin |
Graphs are an integral part of modern data analysis of clinical trial. Viewing data in a graph together with the tabular results of statistical analysis can greatly improve understanding of the collected data. Graphical data display provides insight into trends and correlations that are simply not possible with tabular data; the visual representation can very often be the most informative way to understand the insight results. The spider plot of change in tumor size from baseline is one of the more common graphs in oncology studies. Unlike the waterfall graph, which displays the maximum change from baseline for each subject , the spider plot allows us to visualize change from baseline over the time. Per our experience, the spider plots could also display other clinical meaningful information, such as time-point responses, study drug dosage, and some subject level information, for example, the value of best overall response. This additional information can be very helpful for reviewers | study day, change from baseline, proc sgplot, sdtm domain, proc step, standard deviation, cdisc | source | 14 |
| 80 | Enhancing Define-XML Generation: Based on SAS Programming and Pinnacle 21 Community | Kevin Sun | Define-XML transmits metadata that describes any tabular dataset structure and its creation is an important area for SAS programmers. It involves converting the programming work into an XML file while also ensuring the content accurately represents the derivation or analysis of the programming work in the study. The Pinnacle 21 Community software (version 4.0.1) simplifies the XML file generation process with a one-step button, and it could help programmers focus on the contents of programming. However, this creation does not always display the contents as expected, especially for Define-XML v2.1. This paper describes a method employing SAS code for the automatic enrichment of content with more details, enhancing efficiency and comprehensiveness in the generation process of Define -XML | create table, proc sql, proc step, cdisc | source | 11 |
| 81 | Enhancing FDA Debarment List Compliance through Automated Data Analysis Using Python and SAS | Yongjiang (Jerry) Xu, Karen Xu, Suzanne Viselli |
This paper presents an innovative approach to ensuring compliance with the FDA's debarment list by employing Python and SAS for automated data analysis. The primary objective was to develop and streamline the process of checking individuals and entities against the FDA's debarment list, thereby reducing the risk of regulatory violations. We utilized Python for data scraping and extracting debarment data from the FDA's website, followed by SAS for data analysis and provided a comprehensive compliance solution. The results demonstrated a significant reduction in manual labor and time required for compliance checks, with the added benefit of minimizing human error. The scalable and efficient tool automatically flags if any individuals or entities are on the debarment list from international trials, facilitating timely decision-making. Key words: FDA Debarment List, Regulatory Compliance, Python, SAS, Fuzzy Matching Programming | data science, sample | source | 6 |
| 82 | Estimating Time to Steady State Analysis in SAS | Richard Moreton, Lata Maganti |
For chronic use drugs, the time it takes to reach steady state drug concentrations needs to be estimated. Steady state is said to be reached when after multiple doses of a drug there is no meaningful difference between pharmacokinetic (PK) profiles of successive doses. An overview of three statistical methodologies is presented along with SAS code. These methodologies include NOSTASOT (No- Statistical-Significance-of-Trend), effective half-life for accumulation, and nonlinear mixed effects modeling. NOSTASOT requires the least number of assumptions while effective half -life and nonlinear modeling have several assumptions. To perform the analyses, we will touch upon advanced programming with PROC IML, PROC MIXED, and PROC NLMIXED |
proc step, proc mixed, proc iml, covariance, covariance structure, phase i | source | 8 |
| 83 | Excel Email Automation Tool: Streamlining Email Creation and Scheduling | Xinran Hu, Jeff Xia |
In the evolving landscape of digital communication, efficiency in email management has become paramount for businesses and organizations. This paper introduces the EXCEL Email Automation Tool, a solution designed to automate the drafting and scheduling of emails using Microsoft EXCEL as a front-end interface, seamlessly integrated with Microsoft Outlook. In the intricate landscape of pharmaceutical research and development, accurate and timely communication is paramount. This industry, laden with vast datasets, regulatory requirements, and critical timelines, also grapples with duplicated communication among technical leads, trial managers, and vendor teams. The EXCEL Email Automation Tool emerges as an essential solution to these multifaceted challenges. By bridging the gap between complex data management in Microsoft EXCEL and effective communication via Microsoft Outlook, this tool introduces a streamlined approach to correspondence |
excel, data governance | source | 7 |
| 84 | Experimenting with Containers and webR for Submissions to FDA in the Pilot 4 | André Veríssimo, Ismael Rodriguez |
Pilot 4 of the R Consortium Submission Working Group marks a pioneering step in using R for FDAclinical trial submissions, introducing novel technologies like WebAssembly and Containers to elevatesubmission efficiency and effectiveness. This initiative, in collaboration with Appsilon, is revolutionizingthe Shiny application packaging and transfer process, establishing a newbenchmark that complementsthestaticandextensivedocumentation.At the heart of Pilot 4's innovation are WebAssembly and Containers. WebAssembly enables Rapplications to run almost at native speed in web browsers, bypassing complex local installations andgreatly enhancingaccess. Thisadvancement iscrucial for moreefficient andsecureoperationof R-basedapplications, especiallyunder thestrict regulationsof clinical trials.Containers, notably Podman, allowfor the same benefits of WebAssembly intermsof portabilitywiththeadvantage of being a well-established technology | clinical trial, adam dataset, cdisc | source | 8 |
| 85 | Exploit the Window of Opportunity: Exploring the Use of Analysis Windowing Variables | Richann J. Watson, Elizabeth Dennis, Karl Miller |
For analysis purposes, dataset records are often assigned to an analysis timepoint window rather than simply using the visits or timepoints from the collected data. The rules for analysis timepoint windows are usually defined in the Statistical Analysis Plan (SAP) and can involve complicated derivations to determine which record(s) best fulfils the analysis window requirements. For traceability, there are ADaM standard variables available to help explain how records are assigned to the analysis windows. This paper will explore these ADaM variables and provide examples on how they may be applied | data science, study day, sample, adam dataset, cdisc | source | 4 |
| 86 | Exploring DATALINEPATTERNS, DATACONTRASTCOLORS, DATASYMBOLS, the SAS System REGISTRY procedure, and Data Attribute Maps (ATTRMAP) to assign invariant attributes to subjects and arms throughout a project. | Kevin R. Viel | A subject, arm, or other grouping characteristic can appear in numerous figures, including in various deliveries with updating data, including new subjects or arms. Being able to assign a given subject invariant attributes such as a line pattern, marker, line color, and marker color may assist in the interpretation or review of the figures containing them without burdensome references to a legend. For instance, subjects in the first arm might be assigned blue lines in every plot and USUBJID 1 might be assigned a dashed line with an open diamond symbol. With care, if that subject is enrolled in a new trial, these attributes can remain with her or him, that is, be invariant across trials, or updates (interim and final deliveries). Further, using the SAS System REGISTRY procedure and testing, one can determine which combinations of attributes might be standards for a company |
macro parameter, proc step, macro quoting function, cross join, output delivery system, proc sgplot | source | 22 |
| 87 | Exploring the Application of FDA Medical Query (FMQ) in Visualizing Adverse Event Data | Tongda Che, Danfeng Fu |
The analysis and visualization of adverse event (AE) data is critical for evaluating drug safety in clinical trials. This paper explores the application of the FDA Medical Query (FMQ) using R programming language to conduct safety analyses and create meaningful visualizations of AE data. FMQ provides a standardized approach to identify adverse events of interest. By leveraging FMQ with R, automated safety data workflows can be created to accelerate drug safety reviews. In this paper, adverse event data coded with the MedDRA dictionary was mapped to FMQ list. The resulting analysis datasets are visually represented interactively using R Shiny to display various R graphs, such as Lollipop plots, pie charts, and circular bar charts. These visualizations offer insights into AE incidence, severity, timing, durations, and relationships | adverse events, clinical trial, raw data | source | 12 |
| 88 | Externally Yours - Adeptly Managing Data Outside Your EDC System | Frank Canale | Programmers in the pharmaceutical industry are used to working with data that is entered into, and extracted from, a system commonly known as an EDC (Electronic Data Capture) system. When using data that is sourced from one of these systems, you can reliably count on the type of data you will receive (normally SAS datasets), and if the EDC is set up well, a standard structure that provides output data containing CDISC/CDASH variable names. But what does one do when receiving data that is sourced outside the EDC system and received from other vendors? How do you manage this data…retrieve it…validate the structure…even export it to a format allowing you to merge it with other more conventional SAS datasets? | controlled terminology, dataset, excel, proc step, macro parameter, macro variable, proc import, error message, length statement, create table, out=, macro loop, temporary dataset, proc copy, macro definition, %if-%then-%else, proc sql, edc, cdisc | source | 38 |
| 89 | Facilitating Seamless SAS-to-R Transition in Clinical Data Analysis: A Finetuned LLM Approach | Chaitanya Pradeep Repaka, Santhosh Karra |
The rapid evolution of data analysis tools has presented the clinical industry with both opportunities and challenges, particularly in transitioning from traditional SAS-based environments to the more versatile R programming language. This paper introduces a Language Model (LLM) specifically finetuned to bridge this gap, enhancing the ease of transition for clinical data analysts. Our model is uniquely tailored to understand and convert SAS code into R, focusing on key areas such as Study Data Tabulation Models (SDTMs), Analysis Data Models (ADaMs), and Tables, Figures, and Listings (TFLs). This LLM serves as an intelligent personal assistant, offering in-depth insights into the comparative dynamics of SAS and R coding methodologies, thereby facilitating a deeper understanding and efficient reproducibility of clinical data analysis processes | data science, clinical data, clinical research, privacy, clinical data analyst | source | 6 |
| 90 | Five Reasons To Swipe Right on PROC FCMP, the SAS Function Compiler for Building Modular, Maintainable, Readable, Reusable, Flexible, Configurable User-Defined Functions and Subroutines | Troy Martin Hughes | The FCMP procedure (aka, the SAS function compiler) empowers SAS practitioners to build our own user-defined functions and subroutines-callable software modules that containerize discrete functionality, and which effectively extend the Base SAS programming language. This introduction explores five high - level problem sets that user -defined functions can solve. Learn how to hide a hash object (and its complexity) inside a function, how to manipulate SAS arrays, how to design a format (or informat) that calls a function, how to run a DATA STEP (or SAS procedure) inside of a DATA STEP (aka, DPDD), and how to avoid unnecessary usage of the SAS macro language. Interwoven throughout the discussion are the specific software quality characteristics -such as modularity, maintainability, readability, reusability, and configurability-that are achieved through the design and implementation of FCMP user-defined functions |
proc fcmp, macro facility, macro definition, hash table, sas base, macro function, macro variable, sysrc, array, proc step | source | 11 |
| 91 | Game changer! The new CDISC ADaM domain ADNCA for PK/PD data analysis | Santosh Ranjan | Characterizing the Pharmacokinetic (PK) profile of novel drugs is an integral part of the drug development process, often performed by utilizing the Non-Compartmental Analysis (NCA) as the standard methodology in clinical trials. Unlike other data analyses usually performed by a team of Biostatisticians and Statistical Programmers (BSP) in an application like SAS, NCA requires cross-functional collaboration involving Pharmacokineticist or Pharmacometrician who usually performs the NCA in another specialized scientific application like Phoenix WinNonLin (WNL) or similar. For the sake of simplicity, this paper refers to such applications collectively as WNL. The NCA approach can also be used to analyze Pharmacodynamic (PD) data so when this paper references PK it is assumed that all arguments can be similarly extended to PD data as well | clinical trial, key, compartmental analysis, sample, pk parameter, clinical research, sdtm domain, adam dataset, clinical database, data science, raw data, proc means, cdisc | source | 7 |
| 92 | GenAI to Enhance Your Statistical Programming | Philip Bowsher | data science, clinical programming | source | 13 | |
| 93 | Generation of Synthetic Data for Clinical Trials in Base SAS using a 2-Phase Discrete-Time Markov and Poison Rare Event Framework | Adam Yates, Misti Paudel, Fengming Hu |
Synthetic data for clinical trials has growing utility in clinical and epidemiologic fields, but a persistent concern has been the viability and reliability of producing synthetic data which conforms to the complex nature of biomedical data. Recent successes in synthetic clinical trial data include the use of Synthetic Control Arm (SCA) applications, but the generation of treatment-related data necessarily faces additional scrutiny. While synthetic data cannot replace trial data for scientific discovery, the planning and development phases of clinical trials can benefit from the use of synthetic treatment and control data. This paper describes a novel program developed in base SAS which generates synthetic data that was used in clinical trial development and report programming | clinical trial, clinical data | source | 14 |
| 94 | Generative Artificial Intelligence in sample size estimation - challenges, pitfalls, and conclusions | Igor Goldfarb, Vikas Sharma |
The last couple of years are 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 increase awareness of investigators, scientists and statisticians about challenges and risks in application of GenAI to calculate the sample size for prospective clinical trial. The authors assessed the performance, benefits, and risks of using GenAI for sample size calculations in clinical study design. One of the popular GenAI products - Chat Generative Pretrained Transformer (ChatGPT) - was chosen as a working tool. The sample size given in the completed study (the data wer e taken from ClinicalTrials.gov) was replicated using commercial software nQuery and ChatGPT. The authors found that ChatGPT failed to produce proper outcome (nQuery returned the correct results) while demonstrating a spectrum of errors | sample size, sample, proc power and sample size, artificial intelligence, null hypothesis, type i error, clinical trial, type ii error, exact test, chi-square test, phase ii, fisher exact test, binomial distribution, effect size | source | 26 |
| 95 | Go Get 'Em: Manager's Guide to Make a Winning Business Proposal for Technology Solutions | Dilip Raghunathan | This paper empowers a leader to put together a winning business case to secure resources and funding for delivering technology solutions that enable, enhance, or transform business capabilities. The paper will present a foundational framework that encapsulates the concept of business capabilities and how a leader can represent the priority and maturity of the capability in its current state, and use it as a baseline to set aspirational targets for a future state. The paper will also present a framework by w hich the leader can capture quantitative and qualitative aspects of the target state's business value, and use that as a driver along with the case for urgency and risk of inaction to unequivocally make a wining argument for why the organization should direct its time and resources to the leader's call for action. The paper will finally cover how to develop a plan of action and approach to bringing the technology solution into fruition to improve the business capability | csr, proof of concept | source | 7 |
| 96 | Guidance Beyond the SDTM Implementation Guide | Kristin Kelly, Michael Beers |
A common misconception among preparers of SDTM data seems to be that it is sufficient to just follow the SDTM Implementation Guide when creating the datasets. The truth is that it is more complicated than that. A preparer of SDTM datasets needs to be aware of all the industry guidance available when preparing for regulatory submission, from CDISC and the regulatory agencies, but also from other organizations as well. This presentation will discuss some of the lesser-known guidance in the industry and why they should be referenced, as well as some of the impacts of not using these documents in the creation of SDTM datasets | sdtm domain, sdtmig, clinical trial, data cleaning, medical history, clinical endpoint, data validation rules, cdisc | source | 11 |
| 97 | Guideline for Creating Unique Subject Identifier in Pooled studies for SDTM | Vibhavari Honrao | Demographic (DM) data set is the parent data set which includes a set of essential standard variables that describe each subject in a clinical study. One of these key variables is Unique Subject Identifier (USUBJID). SDTM IG does not provide any guidance on creation of USUBJID for pooled studies. Hence it becomes necessary to understand programming steps involved for statistical programmers. In clinical trials, there are cases wherein subjects are re -enrolled for different studies for the same compound, and it can be difficult to identify the subject while maintaining CDISC compliance. For ISS analysis, pooling of studies becomes challenging d ue to multiple SUBJID, RFICDTC, RFSTDTC, RFENDTC etc. within same USUBJID from different studies. This paper demonstrates various steps and programming logics involved to develop DM data set by taking hypothetical examples from multiple studies and creates pooled data sets | clinical endpoint, clinical trial, cdisc | source | 5 |
| 98 | Handling of Humoral and Cellular Immunogenicity Data in SDTM | Wei Duan | The immunogenicity endpoints have been broadly used and examined in clinical vaccine studies as the key assessment of immune response to the viral infection. On one hand, hum oral immunogenicity, including serum neutralizing and binding antibodies, has been migrated to IS domain per SDTMIG v3.4, as this domain is primarily used to host molecules targeting to antibody recognition, binding, and testing. Along the way of migrating, we faced some challenges when integrating various data sources, from vendor tested immunogenicity results, EDC sample collection data, to central lab data, with added complexity in data flows, discrepancies, and out-of-bound values. On the other hand, there is a trend that clinical vaccine studies pay more and more attention to cellular response, based on reading of CMI (Cell Mediated Immunity) data. Cytokines produced by monocytes, T cells, and lymphokines, play important roles in regulating immune functions and developing antiviral immune responses | sample, clinical trial, data cleaning, sdtm programming, cdisc | source | 6 |
| 99 | Hands-on Python PDFs: Using the pypdf Library To Programmatically Design, Complete, Read, and Extract Data from PDF Forms Having Digital Signatures | Troy Martin Hughes | The pypdf library (https://pypdf.readthedocs.io/en/stable/index.html) facilitates the programmatic creation, editing, completion, and extraction of PDF forms. Using only pypdf, developers can design a PDF form, make the form available to end users, and programmatic ally collect user data. One notable limitation of pypdf is its lack of support for PDFs that require one or more digital signature blocks (https://pypdf.readthedocs.io/en/stable/meta/scope-of-pypdf.html), and similar Python libraries such as pyhanko acknowledge they are "in beta, and not yet production-ready." (https://pypi.org/project/pyHanko/). This hands-on workshop introduces users to the powerful pypdf library, and demonstrates a solution that does enable PDF forms to be generated (and populated) dynamically -including forms that contain digital signature blocks | extract, data governance | source | 7 |
| 100 | Harmony in Motion: Nurturing Work-Life Balance for Sustainable Wellbeing | Purvi Kalra, Varsha Mithun Patil |
In today's fast-paced and interconnected world, achieving a harmonious work-life balance has become a critical pursuit for individuals, organizations, and society at large. This paper delves into the multifaceted dimensions of work -life balance, exploring the intricate interplay between professional responsibilities and personal well-being. The paper begins b y examining the evolving nature of work in the digital age and its impact on individual's lives. It explores the psychological and physiological implications of prolonged work hours, emphasizing the importance of setting clear boundaries to prevent burnout and enhance overall mental health. Furthermore, this paper investigates organizational practices that promote work -life balance, encompassing flexible work arrangements, supportive corporate cultures, and the role of leadership in fostering a healthy work environment. Key attention is given to the role of technology in both enabling and challenging work -life balance | artificial intelligence, health insurance | source | 6 |
| 101 | How to generate a submission ready ADaM for complex data | Yilan Xu, Hu Qu, Tina Wu |
In clinical trial analysis, composite endpoints are quite common in many phase II or phase III studies. There are many challenges to create it, not only for programming but also for understanding perspective. Some complex endpoints require a combination of individual data (CRF collected) and daily data (ePRO). Furthermore, the daily data contains several criteria which involve complicated calculation rules such as threshold, slope and so on. An intermediate dataset proves ideal in these cases, offering more specific information and facilitating traceability and verifiability to the original data. It is a bridge between the multiple sources of original data and the final standard ADaM. This paper will introduce how to create a submission ready data for a composite endpoint by using several intermediate datasets | analysis set, randomization, cdisc | source | 8 |
| 102 | I Want to Break Free: CRF Standardization Unleashing Automation | Laura Fazio, Andrew Burd, Emily Murphy, Melanie Hullings |
Achievingefficient andimpactful CaseReport Form(CRF) standardizationinthepharmaceutical industrydemandsintensecross-functional collaborationandasharedunderstandingof thebenefits. Thisfoundationiscrucial for improveddataqualityaswell asdownstreamanalysisandreportingautomation.Deviationsfromstandardscausemanual review, increasederrors, andaddedinefficienciesindownstreamcodedevelopment. Toaddressthesechallenges, aninternal StandardsCommitteeledbyDataManagement andSystemsAnalyticsteamswasformedtogaindiversecross-functional alignment throughacomprehensivecharter.Thecharter mandatesthat studyteamsadheretostandardsduringstudystartup, withdeviationsrequiringjustificationandapproval fromtheCommittee. WhileCRFstandardsaretypicallydevelopedbyMedical andClinical teams, weadditionallyincluderoleswithafocusondownstreamanalysisandreportingincludingour DataScience, Statistical Programming, andClinical Analyticsteams | clinical research, clinical programming | source | 6 |
| 103 | iCSR: A Wormhole to Interactive Data Exploration Universe | Sudhir Kedare, Steve Wade, Chen Yang, Matthew Travell, Jagan Mohan Achi |
In the past decade, CDISC has brought standardization to clinical data, significantly benefiting clinical development by allowing regulatory reviewers to review submitted data with efficiency and precision. Despite these advances, bottlenecks such as static TFL report submission hinder the overall efficiency of the review cycle. At Jazz, we are continuously seeking innovative ways to explore data to establish a foundation for digital transformation. Namely, we have developed the Interactive Clinical Study Reports (iCSR) application, utilizing CDISC-standardized data and RShiny capabilities to enable interactive data exploration. This paper demonstrates how iCSR facilitates real-time dynamic data exploration and significantly improves the data review process by providing fast insights and enabling informed decision-making. iCSR comes with an intuitive UI and minimal onboarding | proc means, clinical trial, data science, cdisc | source | 7 |
| 104 | If its not broke, don't fix it; existing code and the programmers' dilemma | Jayanth Iyengar | In SAS shops and organizational environments, SAS programmers have the responsibility of working with existing processes and SAS code which projects depend on to produce periodic output and results and meet deadlines. Some programming teams still cling to the old adage; if it not broke, don't fix it. They've come to depend on code which runs clean, and is reliable. However, besides processing with no errors and warnings. there other criteria to judge the quality of a SAS program. Programming guidelines dictate that code should be well- documented, readable, and efficient, and conform to best practices. This paper challenges the conventional wisdom that code which works shouldn't be modified | sas log, proc step, proc printto, sas enterprise guide, sas output, sas consultant, txt, dataset, index, sample | source | 16 |
| 105 | Implementation of composite estimands for responder analysis based on change from baseline in non-solid tumours | Reshma Radhakrishnan | The International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) published E9 R1 "Addendum on Estimands and Sensitivity Analysis in Clinical Trials" in August 2017. An estimand is a precise description of the treatment effect reflecting the clinical question posed by a given clinical trial objective. Description of estimands must be based on how intercurrent events affect clinical question of interest. I ntercurrent events (ICE) are events occurring after treatment start and how it affects either the interpretation or measurement associated with clinical question of interest. Some of examples of ICEs are Death, switch to other treatment, treatment discontinuation etc. There are five strategies for addressing ICEs like Treatment poli cy strategy, Hypothetical strategies, Composite variable strategies, While on treatment strategies and Principal stratum strategies | change from baseline, missing value, intercurrent event, multiple imputation, imputation, proc step, composite strategy, clinical trial, responder analysis, proc mi, primary endpoint, data science, principal stratum | source | 11 |
| 106 | inspectoR: QC in R? No Problem! | Steve Wade, Sudhir Kedare, Chen Yang, Matthew Travell, Jagan Mohan Achi |
More organizations are starting to embrace open-source technologies to perform tasks traditionally completed in SAS. One such activity is to QC datasets, tables, and figures in the process of producing TLF's. Independent programming is done for many of those TLF's, comparing the results from both programmers. Jazz has developed the inspectoR package, an alternative to SAS/COMPARE procedure, to allow QC performed in R to be compared back to datasets coming from a SAS system. In this paper, we demonstrate how inspectoR will compare these datasets and produce a report showing the findings. The report produced by inspectoR is much like PROC COMPARE output but is produced in HTML in a more readable format. inspectoR has proven to be a valuable tool in helping to transition QC tasks to R and maintain the level of quality expected from SAS systems |
ods html, independent qc, proc compare | source | 7 |
| 107 | Integration of SAS GRID environment and SF-36 Health Survey scoring API with SAS Packages | Bartosz Jabłoński | The SF-36v2 Health Survey is a 36 questions survey which allows to measure functional health and well- being from the patient's point of view. As a health survey, SF-36v2 can be used across age, disease and treatment groups, in contrast to disease-specific health surveys, which focuses on a particular disease or condition (see [QM SF36]). Raw SF-36v2 survey data are subject to scoring. Such scoring is provided by tools delivered, for in- stance, by QualityMetric Inc. The QM API provides all the functionality needed to score and save survey data in the PRO Insight Smart Measurement System. Once a survey is scored, the API returns scores and interpretations based upon processed data. In Takeda Pharmaceuticals a SAS○RPackage named QMsf36scoring was developed to facilitate scoring of SF-36v2 data, stored in SAS data sets, by the QM API. The QMsf36scoring package is a generic solution which can be executed on any SAS environment which is able connect to the QM API tool | statistical programmer, sas grid, proc step, sas packages | source | 13 |
| 108 | Integrity, Please: Three Techniques for One-Step Solution in Pharmaceutical Programming | Jason J. Su | In pharmaceutical world, data are frequently reorganized or summarized before the final tabulation with tables or display with listings. SAS programmers typically use a straightforward method composed of three (3) steps: split the data set into different data sets, process them individually, and merge these data sets back, which I called Split-and-combine method. Despite its intuitive structure, the route comes with computational efficiency issues and requires extra efforts to make sure the data structures are compatible in these data sets for avoidance of undetected erroneous results. Here, I summarized three (3) field- tested methods including Do-loop of Whitlock (DoW), self-interleaving techniques, and direct outputting, to circumvent this method with primarily one (1) DATA STEP. The new programs usually contain much less steps and more importantly, less or no intermediate data sets, which greatly saves system resources and reduces the later maintenance efforts |
output, proc sql, analysis set, flag variable, proc means, proc step, hash table, clinical trial, safety population, set statement, by group processing, group by, sas programmer | source | 15 |
| 109 | Interactive Data Analysis and Exploration with composR: See the Forest AND the Trees | Steve Wade, Sudhir Kedare, Matthew Travell, Chen Yang, Jagan Mohan Achi |
Deciding on Post-hoc analyses can be a time-consuming process and it's critical an analysis has been vetted prior to production and release. Jazz is pioneering creative techniques in data discovery using open-source technologies. Accordingly, composR is an interactive R-Shiny application that provides a user-friendly interface for rapid data analysis and exploration. This paper demonstrates how composR allows users to filter data, create new variables, and perform analyses, summaries, and visualizations. It also enables further drill-down into summaries and charts for more in-depth analysis. Although not required, composR supports CDISC standard data from a variety of file types including SAS, XPT, CSV, EXCEL, and Rda. ComposR reduces time, effort and cost over traditional TLF processes by providing insights into analyses prior to formal TLF production. This ensures only necessary analyses are produced, eliminating much of the back-and-forth between departments |
sample, nominal variable, cdisc | source | 7 |
| 110 | Interfacing with Large-scale Clinical Trials Data: The Database for Aggregate Analysis of ClinicalTrials.gov | Joshua J. Cook, Achraf Cohen |
In the realm of healthcare research, leveraging real-world and big data is paramount, especially when utilizing historical clinical trials data to construct probabilistic and predictive models for future research. This paper illustrates an efficient method for mining and analyzing such data from the Aggregate Analysis of ClinicalTrials.gov (AACT) database using tools like R, RStudio, DBI, PostgreSQL, the tidyverse suite, and gt. The AACT database, a publicly available repository containing up-to-date data from registered clinical studies at ClinicalTrials.gov, is instrumental for research in the Real-World Evidence and Big Data domain. Employing R, a comprehensive statistical language, in conjunction with PostgreSQL-a sophisticated open-source relational database system-enables precise data querying. The tidyverse suite, a collection of R packages, is adept at data manipulation and graphic presentation, while gt serves as the gold- standard for table generation | phase i, phase ii, phase iv, clinical trial, phase iii, adverse events, data standard, foreign key, key, relational database, proc sql, data science | source | 16 |
| 111 | Is a Participation-Level ADaM Dataset a Solution for Submitting Integration Data to FDA? | Sandra Minjoe | In 2019, the CDISC ADaM team was prepared to publis h a solution for datasets that support integrated analyses. This document allowed for an "integrated ADSL" dataset, structured as one record per subject per pool. However, the document was never published because it was determined to be challenging for FDA to perform a review when their tools depend on a one-record-per-subject ADSL. There is still no CDISC solution for Integration. In 2022, the CDISC SDS team proposed a standard for handling DM content when a subject participates in a study more than once. It kept DM as one record per subject, allowing the sponsor to determine which of the multiple participations to put into DM, plus added proposed standard domain DC, with many of th e same variables as DM, but structured as one record per subject per participation | adam dataset, open label, sdtm domain, cdisc | source | 7 |
| 112 | Just Stringing Along: FIND Your Way to Great User-Defined Functions | Richann J. Watson, Louise S. Hadden |
SAS provides a vast number of functions and subroutines (sometimes referred to as CALL routines). These useful scripts are an integral part of the programmer's toolbox, regardless of the programming language. Sometimes, however, pre-written functions are not a perfect match for what needs to be done, or for the platform that required work is being performed upon. Luckily, SAS has provided a solution in the form of the FCMP procedure, which allows SAS practitioners to design and execute User -Defined Functions (UDFs). This paper presents two case studies for which the character or string functions SAS provides were insufficient for work requirements and goals and demonstrate the design process for custom functions and how to achieve the desired results |
proc step, proc fcmp, do loop, proc catalog, dictionary tables, user-defined format, format statement, sas function, proc format, sample, data cleaning, proc sql | source | 9 |
| 113 | Kaplan-Meier Graph: a comparative study using SAS vs R | Mrityunjay Kumar, Shashikant Kumar |
Data Visualization plays a very important role in data analysis and reporting. Due to the increase in the high volume of data it has become essential to display data in the form of different types of graphs. Kaplan - Meier graph using PROC LIFETEST is a well -known name to display time-to-event data in the field of clinical SAS programming due to its widespread usage in reporting survival analysis . The purpose of this paper is to explain how Kaplan -Meier (KM) graph is developed and analyzed in both SAS and R in a stepwise manner. Often, customizing the graph to include additional parameters appears challenging in SAS however its comparatively easier in R. Understanding with the help of an example based on two small treatment groups of hypothetical data, new users can understand how the process works and can lead to a pioneer discussion on leveraging open-source software like R |
hazard ratio, survival analysis, proc step, regression analysis, excel | source | 11 |
| 114 | LAST CALL to Get Tipsy with SAS: Tips for Using CALL Subroutines | Lisa A. Mendez, Richann J. Watson |
This paper provides an overview of six SAS CALL subroutines that are frequently used by SAS programmers but are less well-known than SAS functions. The six CALL subroutines are CALL MISSING, CALL SYMPUTX, CALL SCAN, CALL SORTC/SORTN, CALL PRXCHANGE, and CALL EXECUTE. Instead of using multiple IF-THEN statements, the CALL MISSING subroutine can be used to quickly set multiple variables of various data types to missing. CALL SYMPUTX creates a macro variable that is either local or global in scope. CALL SCAN looks for the nth word in a string. CALL SORTC/SORTN is used to sort a list of values within a variable. CALL PRXCHANGE can redact text, and CALL EXECUTE lets SAS write your code based on the data. This paper will explain how those six CALL subroutines work in practice and how they can be used to improve your SAS programming skills | sample, macro variable, call execute, symbol table, call symputx, cpu time, call sortc, local symbol table, date variable, call symput, missing value, numeric variable, call missing, proc sort, macro call, proc step, character variable, sas log, macro put, proc sql | source | 20 |
| 115 | Lead-in and extension trials, how we documented datapoint traceability | Hanne Ellehoj, Veeresh Namburi |
When generating data packages for extension trials, we have faced several challenges. In our case, participants had completed a lead-in trial and had the option to continue into an extension trial. There were two independent Electronic Data Capture (EDC) databases built, one for each trial. When we studied the Statistical Analysis Plan (SAP), we realized that some of the data points collected in the lead-in trial were needed in the reporting of the extension trial. For that we considered our options: we could transfer records from the lead-in trial Study Data Tabulation Model (SDTM) to the extension trial SDTM or from the lead-in trial Analysis Data Model (ADaM) to the extension trial ADaM. The solution chosen was to take data points from the lead-in trial to the extension trial at the ADaM level. In this paper, we share the reflections we had, the decisions we made, and how we implemented and documented both data points and metadata traceability | adam dataset, adverse events, concomitant medications, cdisc | source | 5 |
| 116 | Leadership Lessons from Another Life: How my Previous Career Helped Me as a Statistician | Christiana Hawn, Lily Ray |
Experienced statisticians and programmers recognize that successful clinical research projects depend on more than just technical skills. Statisticians and programmers must collaborate with cross-functional teams, negotiate for defensible methods and reasonable timelines, facilitate productive meetings, and communicate methods and results in a manner accessible to colleagues of different backgrounds. Statisticians and programmers who change careers may appear to be "starting over", and while this is true from a technical standpoint, the value of the leadership and collaboration skills they bring from their former careers should not be overlooked. Prior to becoming a biostatistician, Christiana was a Coast Guard officer, driving ships across the Pacific Ocean. Lily was a social scientist in Alaska, conducting participatory environmental research and leading healthcare program evaluations | clinical research, time series analysis | source | 5 |
| 117 | Leveraging ChatGPT in Statistical Programming in the Pharmaceutical Industry | Ian Sturdy | This paper explores the potential benefits of incorporating ChatGPT, a state-of-the-art natural language processing model, in statistical programming within the pharmaceutical industry. By leveraging ChatGPT's capabilities, this technology can save time, money, and most importantly, your sanity. Programming often leads to frustration, anxiety, and sleepless nights trying to solve complex problems. Various applications and techniques that harness the power of ChatGPT will be described to reduce all of these. In a world where artificial intelligence threatens to take our jobs, this paper suggests methods of tapping into the untapped potential of ChatGPT to empower programmers with innovative tools, thereby increasing their value. When programming issues arise, no longer will you need to worry about judgement or hostility from others on online forums, particularly the wrath experienced when not including a reproducible example | proc step, proc means, dataset, proc freq, logistic distribution, proc sort | source | 24 |
| 118 | Leveraging SAS and Adobe Plug-in for CRF Bookmark Generation (Rave studies) | Swaroop Neelapu | Annotated CRF (acrf.pdf) should be dual bookmarked: (1) bookmarks by chronology and (2) bookmarks by forms. This is usually done manually and is quite tedious, time consuming and an error prone process . This paper will discuss automation that can be leveraged to complete this process in record time with much higher precision and accuracy . This is possible irrespective of the number of pages in the CRF or the number of visits in the trial. It is a completely menu- driven process with very minimal use of keyboard involving only 4 major steps . The resulting bookmarks in acrf.pdf are compliant with the standards mentioned in Metadata Submission Guidelines version 2.0 ( CDISC-SDTM-MSG-v2.0). It also includes dual bookmarking by Visits/Forms including other features like nested levels, running records, inherit zoom magnification, etc | excel, adverse events, cdisc | source | 12 |
| 119 | LLM-Enhanced Training Agent for Statistical Programming | Jason Zhang, Jaime Yan |
This paper presents a cutting-edge training agent for statistical programming, leveraging a Language Learning Model (LLM) to streamline self-learning and skill development. Central to this system are OpenAI's LLM functionalities, tailored prompts, and a comprehensive support document system, integrating GPTs, FDA CDISC guidelines, and specific learning materials. The agent excels in creating personalized learning paths and plans, addressing the common challenge of systematic learning in self-learning environments. It effectively curates relevant materials and conducts assessments, fostering a self-learning culture complemented by interactive workshops. This method not only enhances training efficiency and cost-effectiveness but also ensures statistical programmers are equipped with necessary programming and regulatory compliance skills, promoting continuous professional growth | clinical programming, clinical trial, pharmaceutical industry, sas programming, cdisc | source | 8 |
| 120 | Macro Variable Arrays Made Easy with macroArray SAS package | Bartosz Jabłoński | A macro variable array is a jargon term for a list of macro variables with a common prefix and nu- merical suffixes. Macro arrays are valued by advanced SAS○Rprogrammers and often used as "driving" lists, allowing sequential metadata for complex or iterative programs. Use of macro arrays requires ad- vanced macro programming techniques based on indirect reference (aka, using multiple ampersands &&), which may intimidate less experienced programmers. The aim of the paper is to introduce the macroArray SAS package. The package facilitates a solution that makes creation and work with macro arrays much easier . It also provides a "DATA-step-arrays-like" interface that allows use of macro arrays without complications that arise from indirect referencing. Also, the concept of a macro dictionary is presented, and all concepts are demonstrated through use cases and examples | macro variable, macro definition, sas packages, macro variable array | source | 49 |
| 121 | Managing a Single Set of SDTM and ADaM Specifications across All Your Phase 1 Trials | Avani Kaja | Submitting data in CDISC standards has become mandatory in the drug development industry. The specifications for such standardized data structures and controlled terminology can be managed in metadata collections called data definition tables (DDTs). While it is common practice for many companies to use a single consolidated master DDT (MDDT) to manage all studies within a single product, this paper will discuss how to standardize and implement an MDDT across multiple products in oncology early-stage development (Phase 1) where studies from different products and indications tends to share common design and many analysis endpoints. The paper emphasizes implementation, process, and best practices to accommodate different products into one template. It will discuss the process of setting up and maintaining the specifications across various products | phase i, controlled terminology, cdisc | source | 5 |
| 122 | Methodology for Automating TOC Extraction from Word Documents to Excel | Jeetender Chauhan, Madhusudhan Ginnaram, Sarad Nepal, Jaime Yan |
In the complex realm of large-scale clinical study management, efficient data handling and traceability are paramount. This paper introduces a beneficial tool designed within EXCEL, which utilizes Visual Basic Applications (VBA) to revolutionize this process. The tool's primary functionality is automatically extracting table titles from mock document templates and seamlessly populating them into an EXCEL file. This process is streamlined for convenience: a simple click on the 'Prepare Extract' button enables the tool to prompt the user to select a target mock document, autonomously transferring the relevant content into EXCEL. Furthermore, the tool incorporates a comprehensive trace system, crucial for monitoring clinical studies' progression and validation levels, thereby significantly reducing manual data entry and potential errors. This paper will focus on this innovative tool's technicalities, applications, and benefits in managing complex clinical data |
excel, clinical trial, clinical research | source | 5 |
| 123 | MLNR or Machine Learning in R | Chuck Kincaid | machine learning, logistic regression, regression analysis, data cleaning, logistic distribution, training set, test set, stratified sampling, normalization, proc hpforest, data science | source | 23 | |
| 124 | Multiple Logistic Regression Analysis using Backward Selection Process on Objective Response Data with SAS | Girish Kankipati, Jai Deep Mittapalli |
Logistic regression is a process of modeling the probability of a discrete outcome given an input variable. The most common logistic regression models a binary outcome such as true/false, or yes/no, whereas multinomial logistic regression is used when there are more than two possible discrete outcomes. Logistic regression is a useful analysis method for classification problems, when you are trying to de termine if a new sample fits best into a subgroup. In oncology clinical trials, analyzing objective response data using this type of analysis will help select key covariates and stratification factors particularly when an investigational product is transitioning from first - in-human to late-phase trials. Such factors are selected by determining probability and confidence interval values using regression models. This paper will discuss insights into multiple logistic regression procedures and shows an example of how to select the covariates in an oncology study | regression analysis, logistic regression, logistic distribution, response variable, backward selection, multiple regression, sample, proc logistic, mock shell, proc reg, predictor variable | source | 9 |
| 125 | Navigating Success: Exploring AI-Assisted Approaches in Predicting and Evaluating Outcome of Clinical Trials and Submissions | Ruohan Wang, Chris Qin |
Moving beyond the prevalent application of Artificial Intelligence (AI) in drug discovery in pharmaceutical industry, this paper aims to underscore the broader benefits of AI in predicting and evaluating outcomes of clinical trial and regulatory submissions. With the goal of aiding sponsors to mitigate risk factors for higher success rates and foster cost-effective strategies, it provides a comprehensive brainstorm on 1) Identifying potential issues that might trigger regulatory concerns via AI modeling of historical data; 2) Evaluating clinical trials through multidimensional analysis to enable proactive early interventions that heighten probability of favorable outcomes and boost trial success rate. The discussion extends to the strategic organizational setup and workflow to implement wide-ranging AI techniques and innovative approaches to meet the goal, underlining the cross-functional collaboration and adaptive strategies | clinical trial, machine learning, predictive analytics, regulatory submission, resource allocation, service provider, data cleaning | source | 9 |
| 126 | Next Level Reporting: ODS and Open Source | Chevell T. Parker | This document describes techniques used to get started with the Output Delivery System such as an introduction to the Output Delivery System basics. Also discussed include automating and making the most out of your ODS destinations. This document will also highlight the dynamic capabilities when using ODS. The document will further drill down on the popular ODS EXCEL destination and discuss highlights and limitations of this destination. Finally, this document will discuss open-source integration in SAS and Viya platforms and how this can be used to take your reporting to the next level. Concepts will focus on techniques geared to the data analyst and SAS programmer |
excel, proc step, ods pdf, output delivery system, proc fcmp, sas studio, data cleaning, proc sgplot, proc export, proc report, sample, macro variable, proc python, sas programmer, proc template, options statement | source | 24 |
| 127 | No LEAD Function? Let's Create It! | Xianhua Zeng | In data analysis and statistical modeling, there are situations where the ability to access next observations (look-ahead) for specific variables is essential for making comparisons and calculations. Unfortunately, SAS does not provide a built-in LEAD function for this purpose. This paper presents a custom macro-based methodology to create a LEAD function using the PROC FCMP procedure . To access LEAD values in various scenarios, a versatile macro called "GetLeadValue" is also introduced. It supports obtaining lead values based on a specific by-group in dataset. The macro code is currently hosted on my Github page: https://raw.githubusercontent.com/XianhuaZeng/PharmaSUG/master/2024/GetLeadValue.sas |
proc fcmp, proc step | source | 13 |
| 128 | Oncology ADaM Datasets Creation Using R Programming: A Comprehensive Approach | Ishwar Chouhan | Efficient analysis of clinical trial data is crucial in advancing cancer treatment strategies. This presentation showcases using R programming and packages, such as Admiralonco, as a comprehensive approach to creating ADaM datasets for oncology studies. This methodology spans initial data preparation to submission-ready datasets, ensuring compliance with industry standards and regulatory guidelines. We will cover pertinent areas such as data extraction and cleaning; variable mapping and standardization; ADaM dataset creation and variable derivation; meta-data documentation; quality control and validation, and version control and reproducibility. This systematic and transparent approach aims to enable you to create ADaM datasets, using R programming, consistently, accurately, and compliantly for the analysis of clinical trial data in the oncology domain | clinical trial, pd, adverse events, adam dataset, clinical endpoint, missing value, imputation, overall survival, clinical data, randomization, data cleaning, crosswalk, progression-free survival, quality control, raw data, dataset, cdisc | source | 29 |
| 129 | One size does not fit all: The need and art of customizing SCE and MDR for end users | Shilpa Sood, Sridhar Vijendra |
Intoday'sageof cloudcomputingandAI, it ishardtoimaginetheend-to-enddrugdevelopment processdonewithout theuseof several different systemsand toolsemployedat everystage. Thelast decadehasseenasurgeinthenumber of applicationsandsolutionsinthepharmaceutical industryimprovetheefficiencyof thedrugdevelopment processandshorteningtimetomarket. Theseapplicationsandsolutions cover theentiregamut rangingfromdatacapturesystemstoreportingwizardstoautomatedvalidatorstoSCEsandMDRstoAI-basedSDTMmappers. Likeanyother application, theimplementationof anSCEor MDRfor anorganizationisaverycritical processasit offersanopportunityfor theorganizationtore-thinkthewaytheyget thingsdone. Whilehavingafundamentallyclear purpose, SCEsandMDRsalsoneedtooffer alargeamount of flexibilitytotheorganizationthat adoptsthemsothat itcanbeadaptedtotheorganizationsprocessesandintegratedwiththeir existingsystems | clinical trial, clinical research | source | 6 |
| 130 | Ongoing Trends and Strategies to Fine-tune the CRO/Sponsor Partnership - Perspectives from Statistical Programming | Mathura P. Ramanathan | Sponsor and Contract Research Organization (CRO) partnerships are becoming more frequent, and critical in the management and handling of data from clinical trials. In this paper, we describe various types of these interactions, factors that shape and govern these interactions, and pros and cons of each t ype. Additionally, we highlight how multiple factors on both sides determine the magnitude, length, and scope of the relationships. Outsourcing relationships range from short -term transactions to fully integrated partnerships. While partnerships occur in a variety of clinical trial processes, here we focus on the business processes surrounding the needs of clinical and statistical programming. In this paper, we propose effective strategies for optimizing conditions to foster a fully integrated long -term Spo nsor-CRO partnership that benefits both sides, and enables high quality clinical submissions | clinical trial, clinical research, clinical programming, adam dataset, data governance, cdisc | source | 10 |
| 131 | Opportunities and Challenges for R as an open-sourced solution for statistical analysis and reporting, from vendor's perspective | Peng Zhang, Lizhong Liu, Tai Xie |
In recent years, there is an increasing trend from using SAS to using open-sourced R for statistical analysis and reporting. Several large pharma companies have conducted mock submissions to FDA. This improves the progress of further open-source applications in clinical trial reporting. While most of experience that we can borrow are from large pharmaceutical companies, there are rare literatures and materials about how to start R practice from small biotechnology companies, CRO, or data service companies, e.g. what can be done through R, team building, training procedure, R development focus, system environment and application of R in analysis for clinical trials. In this paper, we will review current practices of R and share our approach about standardization, automation, and further consideration in pharmaceutical industries | pharmaceutical industry, data cleaning, data science, clinical trial, statistical programmer | source | 8 |
| 132 | Optimizing Clinical Data Processes: Harnessing the Power of Metadata Repository (MDR) for Innovative Study Design (ISD) and Integrated Summary of Safety (ISS) / Efficacy (ISE) | Lakshmi Mantha, Purvi Kalra, Arunateja Gottapu |
The implementation of a Metadata Repository (MDR) system in clinical data processes has proven instrumental in significantly reducing costs and development timelines for organizations. MDR adoption facilitates reusability, process automation, and end -to-end data traceability, leading to e nhanced data handling capabilities and process standardization. This paper focuses on leveraging MDR for Innovative Study Design (ISD) and Integrated Summary of Safety (ISS) / Integrated Summary of Efficacy (ISE), presenting use cases that demonstrate its impact on various crucial aspects. 1. Performing a Case Report Form (CRF) compliance check and cloning using a Metadata Repository (MDR) 2. Comparing one study with another using a Metadata Repository (MDR) 3. Compare metadata across medical dictionary for Regulatory Activities (MedDRA) and workflow standards. 4 | data science, crf, adverse events, clinical research, regulatory submission, adaptive design, clinical trial, consistency, data validation rules | source | 9 |
| 133 | Periodic safety reports of clinical trials | Murali Kanakenahalli, Annette Bove, Smita Sehgal |
Safety data is an important part of clinical trials. Periodic safety reporting helps in understanding the safety risk of all investigational products. These reports inform regulators on evolving safety profile of an investigational drug and apprise them of actions to address any safety concerns. Some of the key safety reports are Investigator's Brochure (IB), Development Safety Update Report (DSUR), Periodic Safety Update Report (PSUR). The DSUR is intended as a common standard for periodic reporting on products under development among the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) regions. DSUR is submitted annually. The main objective of a DSUR is to present a comprehensive, thoughtful annual r eview and evaluation of pertinent safety information collected. An IB is a collection of clinical data about the investigational product that is the focus of the study | adverse events, clinical programming, clinical trial, clinical data, lookup, adam dataset, raw data, cdisc | source | 8 |
| 134 | PHUSE Safety Analytics Working Group - Overview and Deliverables Update | Nancy Brucken, Mary Nilsson, Greg Ball |
The PHUSE Safety Analytics Working Group, a cross-disciplinary cross-industry collaboration, is working to improve the content and implementation of clinical trial safety assessments for medical research, leading to better data interpretations and increased efficiency in clinical drug development and review processes. The Working Group has produced numerous deliverables (Conference Posters and Presentations, White Papers, Publications, Blogs, etc.,) over the past 10 years and has many ongoing projects. This presentation will provide an overview of the Working Group and its associated project teams, and share an update of the teams' progress, key deliverables for awareness and a summary of ongoing projects | adverse events, clinical trial, laboratory data, stage 2, trial elements, phase ii, stage 1 | source | 5 |
| 135 | Pictorial Representation of Adverse Events (AE) Summary - A new perspective to look at the AE data in Clinical Trials | Pradeep Acharya, Anurag Srivastav |
In clinical trials, ensuring the safety of the trial subjects is very critical along with the efficacy of the drug. The sponsors are directly accountable for monitoring the safety profiles of the subjects and it is identified by analyzing the Adverse Events (AE). An AE is defined as any un intended change in anatomical, physiological, or metabolic functions as indicated by physical signs, symptoms and/or laboratory changes occurring in any phase of the clinical study whether or not such change is considered drug related. As a practice in clinical trials, the Adverse Events data has been looked through the Summary tables and the Listings. There is a need for greater vigor in the analysis and presentation of the AE data along with the tables/listings to help clinicians to vi sualize and easily understand the pattern of adverse events so that they can write Clinical Study Reports (CSR) more efficiently. In this paper we introduce a new perspective to look at the AEs in the form of Graphs | bar chart, adverse events, system organ class, clinical trial, horizontal bar chart, adverse event summary, nominal variable, line chart, proc step, group by | source | 11 |
| 136 | Piloting data visualization and reporting with Rshiny apps | Yun Ma, Yifan Han |
The pharmaceutical industry is increasingly adopting open-source R and shiny for web-based data visualization and reporting, given its numerous advantages such as easy comprehension, interactive analysis, real-time updates, etc. This paper will provide some real practical examples to illustrate these benefits, such as Exploratory Biomarker Analysis in Statistics, efficacy analysis plots in oncology, real- time safety data monitoring. In addition, we will share the learnings that our team went through in the last two years since we established a team of R programmers within our biometrics function. Our goal was to leverage the R open-source software language for data visualization and reporting across the company. Over this period, we've worked closely with various stakeholders within the medical functions to align our goals and gather user requirements | data visualization, clinical trial, data review, adverse events | source | 7 |
| 137 | Piloting into the Future: Publicly available R-based Submissions to the FDA | Benjamin Straub | In recent years, statisticians and analysts from both industry and regulatory agencies have increased adoption of open-source software such as R. R brings great benefits from its vibrant open-source community by providing a wealth of cutting-edge statistical tools, extension packages for interactive dashboard and documentation as well as adaptability to the latest data science trends. Particularly, the dashboard focused R package Shiny has shown to provide great flexibility and interactivity. However, publicly available drug submissions with the R language as the core analysis language has been lacking and limits wider adoption within the Pharma industry. The R Consortium R Submission Working Group (RCRSWG) seeks to test the concept that a R-based language submission language can be bundled into a submission package and transferred successfully to FDA reviewers. As of May 2024, the RCRSWG has successfully completed three pilot submissions and received FDA CDER response letters | data cleaning, clinical trial, adam dataset, data science, statistical software, regulatory submission, cdisc | source | 9 |
| 138 | Plotting Data by US ZIP Code | David W. Franklin | Plotting frequency of events by zip code is something that is visually easy to understand rather that a set of tables with numbers. This paper introduces a way of generating such a graphic using the SAS supplied file with zip code for mapping purposes and merging this with data to produce a quality plot. The example given in the paper will be the number of public hospitals across the 50 states of the USA | proc sort, proc step | source | 4 |
| 139 | Potentials and Caveats When Using ChatGPT for Enhanced SAS Macro Writing | Xinran Luo, Weijie Yang |
AI language like ChatGPT has impressed and even intimidated programmers. There are discussions of ChatGPT with examples of simple SAS steps and there are descriptions of various usages of ChatGPT without examples, but few papers discuss the use of ChatGPT in SAS macro development with examples. This paper explores the utility of ChatGPT in enhancing the process of writing SAS macros from scratch, using an example of checking SAS log in batch on Windows and comparing the procedure of using conventional search engines. The focus is not only on utilizing ChatGPT's capabilities to provide programmers with initial ideas of program structure when they encounter unusual work request but also on demonstrating its application in developing a robust macro by showing key steps of the conversations between programmers and ChatGPT. Although ChatGPT proves invaluable in offering insights and suggestions, it's imperative to acknowledge certain caveats | macro definition, sas programming | source | 9 |
| 140 | Programming Considerations in Deriving Progression-Free Survival on Next-Line Therapy (PFS2) | Alec McConnell, Yun Peng |
Historically, oncology clinical trials have relied on Overall Survival (OS) and Progression Free Survival (PFS) as primary efficacy endpoints. While OS is often the most desired estimate, it requires many years of follow-up to derive an unbiased estimate from the study. Additionally, even with follow-up, OS estimates are subject to confounding due to subsequent therapies which are commonplace in the treatment of cancer. As a proxy for OS, the European Medicines Agency (EMA) has recommended the evaluation of Progression Free Survival after next line of treatment - also known as Progression Free Survival 2 (PFS2). According to the EMA, "PFS2 is defined as the time from randomization (or registration, in non-randomized trials) to second objective disease progression, or death from any cause, whichever first." Despite this definition, PFS2 requires complex data collection and derivation | adam dataset, randomization, imputation, overall survival, progression-free survival, cdisc | source | 5 |
| 141 | Programming with SAS PROC DS2: Experience with SDTM/ADaM | Jianfeng Wang, Li Cheng |
PROC DS2 is a procedure introduced with SAS Base 9.4. This procedure provides opportunities for SAS programmers to apply Object Oriented Programming (OOP) and multithread techniques in SAS programming and is a critical connection between 'traditional' SAS programming and programming in the SAS Viya platform. The goal of this paper is to pilot the use of PROC DS2 in the work of preparing clinical trial CDISC datasets. In this paper, PROC DS2 is tested in the programming of SDTM/ADaM on a server with SAS Base 9.4 M3 release. After converting SDTM/ADaM programs written in 'traditional' SAS programming language into the PROC DS2 code, this paper presents the lessons learned and the notes taken when the obstacles were overcome or bypassed. Furthermore, OOP and multithread techniques are explored to apply to the programming for SDTM/ADaM. Programming setups with a standard folder structure are discussed and the performance of using OOP and multithread techniques are also evaluated |
proc step, sas base, sas programmer, data step, sas programming, adam dataset, clinical trial, sas function, proc sql, cdisc | source | 6 |
| 142 | Protocol Amendments and EDC Updates: Downstream impact on Clinical Trial Data | Anbu Damodaran, Ram Gudavalli, Kumar Bhimavarapu |
This paper investigates the impact of collection database updates during a clinical study conduct, particularly emphasizing Electronic Data Capture1 (eDC) updates or migrations2 made post initial go-live and Protocol amendments3,4. Through examination of practical examples, it reveals the cascading effects on Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM) datasets and regulatory submissions, as well as the resulting modifications in subsequent analysis and reporting. Moreover, the paper scrutinizes the downstream impacts of subject transfers across studies or sites, uncovering intricacies related to re-screening subjects who initially did not meet inclusion/exclusion criteria. By unraveling the complexities of these processes, the paper makes a humble attempt to offer valuable insights to possible scenarios, improve data integrity and ensure compliance with regulatory guidelines in clinical research | clinical trial, protocol amendment, sdtm programming, adam dataset, data integrity, edc, missing value, clinical research, sdtm domain, cdisc | source | 11 |
| 143 | Quality Assurance within Statistical Programming: A Systemic Way to Improve Quality Control | Todd Case, Margaret Huang |
This paper is a discussion of how Quality Assurance (QA) can be used to improve the Quality Control (QC) process within Statistical Programming. QC in this paper is defined to mean independent programming, meaning two programmers use the same Protocol, Statistical Analysis Plan, and other related documents to generate the results as specified in the Table, Listing and Figure (TFL) templates for a specific analysis. Ideally, as QC is independent and the documentation clear, the results between programmers will be identical. That said, when was the last time you QC'd the results of a study and everything was 100% correct, the very first time it was programmed? This is why we will talk about Quality Assurance, which in this paper is defined as using the raw data to generate data that is used to create the same TFLs as the two independent programmers | quality assurance, clinical programming, raw data, data science | source | 4 |
| 144 | Quarto 1.4: Revolutionizing Open-source Dashboarding Capabilities | Joshua J. Cook, Kirk Paul Lafler |
The release of Quarto 1.4 marks a significant advancement in the realm of data visualization and reporting, introducing powerful dashboarding capabilities that cater to the evolving needs of data analysts. This abstract presents an introduction to utilizing Quarto for dashboard creation, emphasizing the importance of this development for the pharmaceutical and biotech industries. Dashboarding in Quarto 1.4 is pivotal, as it allows for the integration of complex data into cohesive and interactive visual summaries. This paper will explore the innovative features of Quarto's dashboarding tool, which simplifies the process of synthesizing large datasets into accessible and actionable insights. Quarto dashboards support a variety of output formats, which enable analysts to communicate their findings to a broad audience, ranging from high-level stakeholders to the general public | clinical trial, data visualization, bar chart, line chart, primary endpoint | source | 17 |
| 145 | R Shiny and SAS Integration: Execute SAS Procs from Shiny Application | Samiul Haque, Jim Box |
The integration of different programming languages and tools is pivotal for translational data science. R Shiny is the most popular tool for building web applications in R. However, biostatisticians and data scientists often prefer to leverage SAS. Procs or macros for clinical decision making. The world of R Shiny and SAS does not need to be decoupled. R Shiny applications can incorporate SAS procs and analytics. In this work, we present mechanisms for integrating R Shiny and SAS. We demonstrate how SAS Procs and macros can be executed from R Shiny front end and SAS logs and results can be printed within Shiny App | proc step, sas viya, sas log | source | 6 |
| 146 | Readlog Utility: Python based Log Tool and the First Step of a Comprehensive QC System | Zhihao Luo | Log checking is a crucial part of SAS programming quality control. To make this process smoother and more robust, I have developed a python-based log checking and summarization tool to provide a better alternative for different user scenarios. The tool provides an improved graphical user interface compared to traditional log reviewing method; it can highlight all important log messages that the user feeds into the application in batches; it creates a summary for different programming folders or even all programming folders under a reporting effort; it also serves as an input for the comprehensive QC tool that is currently in development. This paper is a journal of how the ReadLog Tool was developed: I will talk about the thought process behind the planning of this tool, the programming logic of how this tool works, how different components were developed to achieve various functionalities, and an in-depth look at some of the Python codes used along the way | quality control, clinical programming, sas log, sample, macro definition, sas programming, data cleaning | source | 20 |
| 147 | Reconstruction of Individual Patient Data (IPD) from Published Kaplan-Meier Curves Using Guyot-s Algorithm: Step-by-Step Programming in R | Ajay Gupta, Natalie Dennis |
Secondary analysis may require the use of reconstructed patient-level data from published Kaplan-Meier (KM) curves to support a number of different objectives, including indirect treatment comparisons within the context of economic evaluations. Guyot (2012) developed an algorithm that reconstructs individual patient data (IPD) for time-to-event endpoints using published KM curves. This presentation will provide step-by-step instructions and a use case for executing the Guyot (2012) algorithm to rec onstruct IPD from published KM curves in R | time-to-event endpoint, data mining | source | 10 |
| 148 | Recruiting Neurodivergent Candidates using the Specialisterne Approach | Patrick Grimes | This paper explores the utilization of Specialisterne, a neurodiversity-focused recruitment and consultancy organization, in the process of hiring neurodivergent candidates within a Statistical programming group. Neurodivergent individuals possess unique cognitive strengths, including exceptional attention to detail, pattern recognition, and logical reasoning, which are highly applicable to roles requiring complex data analysis and programming skills. The aim of this paper is to document the experiences and outcomes of incorporating neurodivergent candidates into the Statistical programming group and to evaluate the effectiveness of the Specialisterne approach. The paper discusses the identification of suitable candidates through specific assessment methodologies tailored to the needs and abilities of neurodivergent individuals | phase ii, phase iv | source | 5 |
| 149 | Reimagining reporting and Visualization during clinical data management | Vandita Tripathi, Manas Saha |
Clinical Data Management is a pivotal process in clinical trial, and during clinical data management, it's crucial to monitor the analytics of the study parameters, which may include statistical analysis of the captured patient data as well as management data like audit trails and queries. Reports and dashboards are the primary means by which a reviewer / monitor can get access to all these required analytics from a clinical trial management system. Access to quality reporting data has a critical influence on data management and study success. Clinical trial management systems come with a wide variety of reports that enables the users to get insights on various parameters of the study. Most of the reports are pre-defined and offers little or no configuration options to fine tune the report fields and filters | clinical trial, proc sql, recommendation engine, data standard, machine learning, data governance, data science | source | 9 |
| 150 | Relative Dose Intensity in Oncology Trials: A Discussion of Two Approaches | Christiana Hawn, Dhruv Bansal |
This paper will explore the unique challenges of calculating relative dose intensity in early-phase oncology studies. Relative dose intensity, defined as the actual dose received divided by the prescribed dose according to the protocol, is an important endpoint in clinical trials. This is a straightforward calculation for drugs with daily pill administration, however it becomes more complicated when dealing with intravenous (IV) drugs on varying dosing schedules and cycle lengths as is common in early-phase oncology studies. This paper will present a mock scenario representing a typical early phase oncology study with multiple schedules and explore two different methods of calculating relative dose intensity. The first approach counts the number of expected doses based on the number of cycles completed compared to the number of doses received. This is a straightforward calculation and easy to interpret but does not account for time factors such as dosing delays and skipped cycles | dose intensity, dose, adverse events, per protocol | source | 5 |
| 151 | Relax with Pinnacle 21-s RESTful API | Trevor Mankus | Harness the power of Pinnacle 21 Enterprise's REST API and relax! Programmatically extract metadata from within your validation environment, from study level specifications to broader organizational-level metadata like as Standards and Terminologies. The metadata retrieved includes datasets, variables, value level, codelists, terms, methods, comments, and even Analysis Results Metadata (ARM) for ADaM studies. These objects can then be incorporated into the programming process, such as setting up attributes for variables, assigning dataset labels, establishing key variables, and pairing coded and decoded values. This paper will detail how to access the Pinnacle 21 Enterprise REST API and discuss best practices on implementation within the data standardization process. It will also cover the various options for export file formats as well as provide examples for practical use of metadata to ensure automating metadata retrieval is a breeze | rest api, proc step, proc http, excel, data cleaning, cdisc | source | 8 |
| 152 | Replicating SAS Procedures in R with the PROCS Package | David J. Bosak | The procs package aims to simulate some commonly used SAS procedures in R. The purpose of simulating SAS procedures is to make R easier to use and match statistical results. Another important motivation is to provide stable tools to work with in the pharmaceutical industry. The package replicates several of the most frequently used procedures, such as PROC FREQ, PROC MEANS, PROC TTEST, and PROC REG. The package also contains some data manipulation procedures like PROC TRANSPOSE and PROC SORT. This paper will present an overview of the package and provide demonstrations for each function |
proc step, sample, proc freq, proc sort, proc transpose, by group processing, proc reg | source | 10 |
| 153 | Safety Signals from Patient Narratives PLUS: Augmenting Artificial Intelligence to Enhance Generative AI Value | Sherrine Eid, Sundaresh Sankaran |
Natural Language Processing (NLP) is integral to most Artificial Intelligence solutions that classify and extract events from patient narratives. Challenges arise from their unstructured, labor-intensive and complex nature. Users need to contextualize, reconcile and validate results to establish accurate insights. Large Language Models (LLMs) is a component of Generative AI and presents immense possibilities to ease and enhance safety signal analysis. However, they are imperfect due to lack of context, limited representative data and inadequate data preparation. In this paper, we demonstrate NLP to enhance the value of Generative AI. First, we present a process workflow making use of SAS Viya, open-source LLMs and vector databases. Next, we demonstrate how NLP helps establish accurate and governable embeddings, a key component in data hydration and inferencing. Entity definition & extraction techniques reduces noise and enhance relevance of queries | adverse events, machine learning, artificial intelligence, sas viya, serious adverse event, life sciences | source | 14 |
| 154 | SAS Super Duo: The Program Data Vector and Data Step Debugger | Charu Shankar | Whether you are a self-taught SAS learner with a lot of experience, or a novice just entering the SAS universe, you may not have spent a lot of time delving into two fantastic SAS superpowers. The Program Data Vector (PDV) is where SAS processes one observation at a time, in memory. The DATA STEP Debugger is an excellent tool to actually see the observation being held in memory and watch the movement of data from input to memory to output. Combining these two tools supplies SAS practitioners a lot of utility to "get under the hood" of how SAS code works in practice to ingest and analyze data during program operations. Once you know the specifics of what happens during compile time / execution, joins, and creating arrays, efficient SAS code will be at your fingertips. Action packed with animations, live demos and a great hands on section, this presentation will likely be a resource that you will use and reuse now and in the future |
compilation phase, data step, execution phase, data wrangling, proc sql | source | 6 |
| 155 | SASBuddy: Enhancing SAS Programming with Large Language Model Integration | Karma Tarap, Nicole Thorne, Tamara Martin, Derek P. Morgan, Pooja Ghangare |
SASBuddy, dubbed "your friendly SAS programming assistant," is a pioneering SAS macro designed to facilitate SAS programming through Large Language Models (LLMs). This tool empowers users, particularly those with limited SAS coding expertise, to generate precise SAS code efficiently by interpreting natural language inputs. The core of SASBuddy's functionality lies in its ability to provide contextually accurate SAS code, tailored to specific datasets based on user queries | sas programming, pharma programming, clinical sas, macro definition | source | 8 |
| 156 | SDTM Specifications and Datasets Review Tips | Wanchian Chen | SDTM requirements are spread across various sources such as the Study Data Tabulation Model Implementation Guide (SDTMIG) domain specifications section, SDTMIG domain assumptions section, FDA Study Data Technical Conformance Guide, and PMDA Technical Conformance Guide on Electronic Study Data Submissions. While Pinnacle 21 can assist in identifying issues with SDTM data, it is important to note that data is often limited at the early stages of a study. The most efficient process would be to review SDTM specifications before the creation of SDTM programs, to minimize program modifications and save time. Programmers often seek guidance on conducting a comprehensive review of SDTM but are unsure of where to start. In this paper, I will provide a concise summary of frequently seen findings observed in multiple studies when reviewing SDTM | adverse events, subject visits, cdisc | source | 7 |
| 157 | Semi-Automated and Modularized Approach to Generate Tables for Clinical Study - Categorical Data Report | William Wei, Shunbing Zhao |
A table, listing and figure (TLF) package, incorporated in a clinical study report (CSR), serves to encapsulate the data from a clinical trial. Tables constitute the majority of the TLF package. The CSR tables, which primarily summarize safety and efficacy data, can be categorized into two major modules 1) categorical (gender, sex, race, etc.) and 2) continuous (age, time to event, etc.). This paper explores and discusses a design of the first major module - categorical, which could generate multiple output formats to accommodate the report requirements. The design of this module is independent, flexible, portable, informative, and parameter-driven, and can be exported or linked to different reports. This module can be used independently to produce a categorical table report in different formats. It also can produce different output formats and the reports can be combined for a more complex table | nominal variable, csr, clinical trial, confidence interval, interval variable, sample | source | 8 |
| 158 | Shift gears with "gt": Finely tuned clinical reporting in R using "gt" and "gt summary" packages | Rajprakash Chennamaneni, Sudhir Kedare, Jagan Mohan Achi |
Efforts are underway to use open-source technologies like R and Python for FDA regulatory submission. Based on ongoing initiatives with FDA, there is a strong likelihood that FDA will embrace regulatory submissions utilizing open-source technologies along with SAS. There are numerus existing and emerging packages in R for presenting data in tabular format. This paper will focus on { gt} and its extension {gt summary} package. "gt" package framework uses table header, stub, column labels, spanner column labels, table body and table footer components to create summary reports. We will use these components to demonstrate the ease and flexibility of developing clinical reports. Furthermore, we will leverage "gt summary package" to generate descriptive statistic tables, efficacy outputs, inline tables, and a few custom tables | proc means, nominal variable | source | 6 |
| 159 | Simplifying Edit Check Configuration | Vandita Tripathi, Manas Saha |
Clinical Data Management is a pivotal process in clinical trial, and during clinical data management, majority of the data is directly captured into electronic case report forms (eCRFs). Without good quality data, there is no accurate statistical analysis. Hence maintaining Data Integrity and data quality in the eCRF Forms has a critical influence on data management and study success. Edit checks are the first line of defense against manual data entry errors. They are small programs or algorithms that are used to validate the data to make sure it meets standards/requirements, these programs alert the user that data entry fields have (or may have) errors. Hence setting up and configure the edit checks correctly is a crucial step in designing eCRF forms. In most eCRF Form designing solutions, configuring the edit checks is a cumbersome process | data validation rules, data cleaning, clinical trial, machine learning, recommendation engine, cdisc | source | 7 |
| 160 | Soft Skills to Gain a Competitive Edge in the 21st Century Job Market | Kirk Paul Lafler | The 21st-century economy is converging with existing technologies like proprietary and open-source software, the Internet, robotics, and the Cloud; with emerging technologies like artificial intelligence (AI), machine learning (ML), IoT, nanotechnology, and biotechnology. And to add to all this is the collection, curation, and processing of vast quantities of data and information - all converging at breakneck speeds resulting in the blurring of boundaries between humans and machines. The 21st century job market is experiencing a technological revolution that is changing faster than ever, and organizations, along with today's workforce, must learn how to cope and adapt. Keywords: 21st century, job market, hard skills, soft skills, communication, critical thinking, leadership, teamwork, work ethic, attitude | source | 4 | |
| 161 | Source Data Quality Issues in PopPK/PD Dataset Programming: a Systematic Approach to Handle Duplicates | Ibrahim Priyana, Ellen Winckelmans |
In a perfect world, source data used for PopPK/PD dataset programming would be complete and seamless, but practice teaches us that this is rarely the case. One of the most prevalent reasons affecting data quality is data duplication. This issue can present a challenge for programmers when preparing th e datasets as it causes merging errors, failure of standard programming logic, and could increase the complexity of the program, resulting in more time and effort spent on programming. The standard approach for PopPK /PD datasets is commenting out records containing data quality issues and record its reason in a specific variable. However, the decision of omitting data from the dataset and how to handle the data quality during programming requires careful consideration since it can have a significant impact on the accuracy and integrity of the analysis. It's therefore crucial for both the PK analyst and PKPD programmer to be aware of the risks associated with the decisions taken | data cleaning, sample, raw data, adam dataset, concomitant medications, temporary dataset, proc sql, sdtm domain, cdisc | source | 10 |
| 162 | Splashy Graphics Suitable for Publication? ODS LAYOUT Can Do It! | Dave Hall | The TLFs are done. Pinnacle 21 and the CDISC package is done. Questions have been answered. It's all been delivered. You think it's all over when the Marketing people come to you. "We need some fancy graphs and plots for publication in a magazine! A page full of the ones you did for the submission, but shiny and flashy!" You start to say "SAS can't do that. We need some high-edge software for what you're talking about." But that's not you. What are you going to do? So you think SAS can't create the kind of copy that Marketing is talking about? Think again. By adding just a few simple ODS LAYOUT statements, the same chunks of code you used for the clinical study report figures can be combined in a program to produce a stunning page of spectacular graphics. If you've already created the figures, you've done the hard part |
output delivery system, ods pdf, cdisc | source | 6 |
| 163 | Standardization of the Patient Narrative Using a Metadata-driven Approach | Kuldeep Sen | Patient narratives are important components of any Clinical Study Report (CSR) and are attached as "Attachment I" in the CSR. They are part of the FDA's and the ICH's requirements to provide information about safety of a study - specifically the events of death, serious adverse events, and other adverse events of clinical importance at patient level. The patient narratives have become one of the statistical programming deliverables, and creating high-quality narratives can be a challenging task due to several reasons such as unconventional nature of the narrative template, dynamic nature of data at subject level, and limited knowledge of its scope within a statistical programming team. This paper presents a metadata-driven end-to-end approach, which begins with standardization of the narrative template and employs metadata-driven programming automation. This approach has been shown to greatly enhance the consistency and efficiency in creating the patient narratives | adverse events, clinical programming, in=, sas function, statistical programmer, study day | source | 8 |
| 164 | Standardizing Validation Data Sets (VALDS) as matrices indexed by Page, Section, Row, and Columns (PSRC) to improve Validation and output creation and revisions. | Kevin R. Viel | Table and listing (TL) shells are blueprints specifying the dimensions of the output: PAGES, SECTIONS, ROWS, and COLUMNS (PSRC), their orders, and the precision of the values in the cells indexed by those dimensions. Values may be a composite such as Mean (SD). Viel introduced the Validation Data Set (VALDS), a standardization data set derived and used as input to create output (RTF/PDF). Importantly, the VALDS formalizes the structure and format of such data sets, standardizing 1) TL programs of various types and projects, 2) their Validation, and 3) the manual (spot) checking of the output against the VALDS. The programmer (independently) derives a value and inserts or "pigeon holes" it into its respective cell in the VALDS. For instance, a derived value may be assigned to the cell indexed as COLUMN 3 of PAGE 2, SECTION 3, and ROW 1 of the TL | proc freq, proc step, data cleaning, proc sql | source | 25 |
| 165 | Streamlined EDC data to SDTM Mapping with Medidata RAVE ALS | Yunsheng Wang, Erik Hansen, Chao Wang, Tina Wu |
The manual process of annotating case report forms (CRFs) and mapping raw clinical trial data to the Study Data Tabulation Model (SDTM) standard is both resource-intensive and susceptible to human error. This traditional approach also lacks efficient traceability between CRFs, SDTM mappings files, and final SDTM datasets. This paper explores an innovative method to streamline the mapping process from Electronic Data Capture (EDC) to SDTM by utilizing the Medidata RAVE Architecture Loading Specification (ALS) file. The ALS can be customized to establish links between EDC specifications, CRF, SDTM mapping specifications, and SDTM datasets, creating a seamless end-to-end data flow. By leveraging the customized ALS file, this innovative approach enhances traceability, accuracy, and overall efficiency compared to traditional manual methods. The demonstration of the automated mapping process covers the entire spectrum from EDC data collection to the generation of SDTM define XML | sdtm programming, define-xml, cdisc | source | 12 |
| 166 | Streamlining Patient-reported outcome (PRO) data standardization & analysis | Varsha Mithun Patil, Mrityunjay Kumar |
Patient-reported outcome (PRO) measures is one of the Clinical O utcome Assessments (COA) measures with the aim to capture health- related quality of life from a patient's perspective and without the interpretation of caregivers in contemporary clinical trials. Today, the preferred mode for the collection of PRO data in clinical research is electronic. This preference is driven by the factors like enhancements to data quality that ePRO data collection affords, real -time monitoring, less missing data, and the possibility for immediate interactions. These quality enhancements are compromised using inconsistent data structures and non-adherence to establish data standards. Currently, PRO data are not required to follow a standard model, and the data models used often vary by COA provider and sponsor. Due to non- uniformity in data structure, it often poses risks for programming, analysis, and challenges in submission activities | data science, data cleaning | source | 9 |
| 167 | Study Start Date - Let-s Get it Right! | David C. Izard | The US FDA incorporated a clear definition of Study Start Date into binding guidance in December 2014 and have since reinforced it with language added to the Study Data Technical Conformance Guide. Despite these efforts, Sponsors and Service Providers struggle to correctly and consistently establish Study Start Date, leaving them open to Technical Rejection Criteria firing and standards compliance issues during regulatory review. This paper and highly interactive presentation will clearly establish what Study Start Date is and isn't based on regulatory references, elucidate why it is a data driven study metadata element, discuss the impact of not setting the Study Start Date correctly across your submission elements, and consider similar concepts for filings to other regulatory authorities | clinical trial, data standard | source | 6 |
| 168 | Submitting Patient-Reported Outcome Data in Cancer Clinical Trials - Guidance for Industry Technical Specifications Document | Flora Mulkey | Presentation | disposition table, bar chart, clinical trial, intercurrent event, data visualization, missing value, change from baseline, raw data, baseline, adam dataset, safety population, sample, per protocol, trial summary, proc means, adverse events, controlled terminology, clinical research, sensitivity analysis, cdisc | source | 48 |
| 169 | Tales From A Tech Support Guy: The Top Ten Most Impactful Reporting and Data Analytic Features for the SAS Programmer | Chevell T. Parker | This paper helps you get the most out of SAS software by discussing some features that can make a big impact on your job as a SAS programmer or a data analyst. Some of the areas covered include the following: features that highlight the power of SAS reporting, which allow SAS programmers to generate customized reports SAS integration with open-source tools, as well as automation * some of the most impactful features in SAS Viya | proc step, sas viya, cas, macro variable, sas studio, excel, rest api, dataset, libname engine, proc python, sas programmer, proc import, bar chart, proc http, proc sql, macro definition, proc report, cas action, connection string, output delivery system, call execute, macro loop, proc sgplot, sas enterprise guide, macro facility | source | 23 |
| 170 | Ten Rules for Better Charts, Figures and Visuals | Kirk Paul Lafler | The production of charts, figures, and visuals should follow a process of displaying data in the best way possible. However, this process is far from direct or automatic. There are so many ways to represent the same data: histograms, scatter plots, bar charts, and pie charts, to name just a few. Furthermore, the same data, using the same type of plot, may be perceived very differently depending on who is looking at the figure. A more inclusive definition to produce charts, figures, and visuals would be a graphical interface between people and data. This paper highlights and expands upon the work of Nicolas P. Rougier, Michael Droettboom, and Philip E. Bourne by sharing ten rules to improve the production of charts, figures, and visuals. Keywords: sas, charts, figures, visuals, SGPLOT, sgscatter, sgpanel, bar, box, bubble, dot, histogram, line, pie, scatter, series, vector |
bar chart, proc sgplot, histogram, proc step, scatter plot, proc sgpanel, excel, vertical bar chart, box plot, bubble plot, proc freq, data science, series plot, class variable, correlation | source | 19 |
| 171 | The 5 CATs in the Hat - Sleek Concatenation String Functions | Kirk Paul Lafler | SAS functions are an essential component of the SAS Base software. Representing a variety of built -in and callable routines, functions serve as the "work horses" in the SAS software providing users with "ready -to-use" tools designed to ease the burden of writing and testing often lengthy and complex code for a variety of programming tasks. The advantage of using SAS functions is evident by their relative ease of use, and their ability to provide a more efficient, robust, and scalable approach to simplifying a process or programming task. SAS functions span several functional categories, including character, numeric, character string matching, data concatenation, truncation, data transformation, search, date and time, arithmetic and trigonometric, hyperbolic, state and zip code, macro, random number, statistical and probability, financial, SAS file I/O, external files, ex ternal routines, sort, to name a few | sas function, character functions | source | 3 |
| 172 | The Art of Defensive SAS Programming | Philip R. Holland | macro variable, data cleaning, proc sql, proc step, missing value, txt, select, dataset, numeric variable, sample, macro call, unique key, dictionary tables, macro definition | source | 28 | |
| 173 | The Missing(ness) Piece: Building Comprehensive, Data Driven Missingness Reports and Codebooks Dynamically | Louise S. Hadden | Reporting on missing and/or non-response data is of paramount importance when working with longitudinal surveillance, laboratory, serological, and medical record data. Reshaping the data over time to produce "missingness" statistics is a tried and true technique, but through using metadata and little known variations of familiar SAS procedures, combined with clever ODS reporting techniques, there's an easier way. This paper and presentation will speed up your data cleaning reconnaissance and reporting, and help you find your missing(ness) piece. Additionally, the same techniques will be used to demonstrate how to create robust and utile data dictionaries |
proc step, missing value, proc freq, proc report, proc univariate | source | 15 |
| 174 | The New Shape Of SAS Code | Charu Shankar | What is SAS Code? How many SAS languages exist to manipulate data? If these questions got you thoughtful, then this presentation is just for you. Come explore the many dimensions of SAS code, the commonality and differences between the various languages in SAS. this presentation you will learn the basics of 4 familiar SAS languages, like the Base SAS DATA STEP, PROC SQL, PERL language elements, the SAS Macro language plus a 5th, an introduction to the language of CAS(cloud analytic services). . Learn to check your data with elegant techniques like Boolean logic in PROC SQL, operators in the DATA STEP/PROC STEP, functions like the SCAN function within the DATA STEP, efficient checking of your data with PERL regular expression, and last but not least the amazing marriage between PROC SQL & the SAS macro language to reuse data. You will also learn where CAS can be beneficial for manipulating data in the clou |
proc sql, proc step, macro variable, cas, sas viya, data step, macro facility | source | 16 |
| 175 | The Role of the Blinded Programmer in Preparation of Data Monitoring Committee Packages (for Clinical Trials) | Binal Mehta, Patel Mukesh |
A data monitoring committee (DMC) is a group representing clinicians and biostatisticians having expertise in clinical trials and are appointed by study sponsors to provide an independent assessment of the safety, scientific validity, and integrity of clinical trials. In th e United States, the FDA requires the sponsors to establish a DMC in all clinical trials that assess new treatment interventions. The FDA also strongly recommends establishing a DMC in clinical studies which have double blinded treatment assignment, have substantial safety concern, or have a significant impact on clinical practice. Blinding in clinical trials is a procedure in which one or more personnel are unaware of which treatment participants are receiving, a technique that maintains the ethics and int egrity in a study and reduces the bias in design or execution of the trial and its results | extract, clinical trial, adam dataset, clinical data, database lock, data cleaning, cdisc | source | 6 |
| 176 | The SAS Genome - Genetic Sequencing | Oliver Lu, Katie Watson |
After COVID-19 caused a global shake-up, a growing number of pharmaceutical companies began to place increasing emphasis on genetic sequencing alongside their requested sample testing. Sequencing data outputs can often contain a gargantuan amount of data in various formats, and there may be customization requests to contend with as well. As a programmer you may need to convert the files and organize the data into a comprehensive SAS dataset or report | sas programming, macro variable, sample, proc import, proc step, dictionary tables | source | 10 |
| 177 | The Survival Mode | Varsha Ganagalla, Natalie Johnson |
Researchers often overlook the impact of disease symptoms, diagnostics, treatments, and outcomes in survival analysis as time remains the ultimate focal point. However, the observation of an event in most subjects might be due to various reasons, to name a few - the subjects might have been censored from the study due to withdrawal, lost to follow up, erroneous enrollment and/or at the end of the study the event hasn't been observed at all. Censoring makes it difficult to choose the right technique to account for probabilities. Kaplan -Meier survival estimates do a better job at handling censored data by providing survival probabilities and survival curves for groups of interest. On the contrary, it lacks the ability to inform on the significance of difference in probabilities of two or more groups unless specified explicitly using different options | proc step, confidence interval, clinical trial, kaplan-meier plot, response variable, histogram, censoring | source | 12 |
| 178 | Tips for Completing Macros Prior to Sharing | Jeffrey P. Meyers | SAS macros are a programmer's best friend when written well, and their worst nightmare when not. Macros are a powerful tool within SAS for automating complicated analyses or completing repetitive tasks. The next step after building a capable tool is to share it with others. The creator of the macro does not have much time to catch the attention of the user. The user encountering multiple errors, no documentation or guides, and lack of intuitive features pushes the user away from the macro. This paper will focus on completing a macro to give the user the best possible experience prior to sharing | error message, macro variable, macro loop, %if-%then-%else, macro parameter, macro definition, macro function, macro put, proc sql, delete, macro call, proc step | source | 17 |
| 179 | Towards understanding Neurological manifestations of Lyme disease through a machine learning approach with patient registry data | Lorraine Johnson, Lara Kassab, Jingyi Liu, Deanna Needell, Mira Shapiro |
disease occur annually. Many patients remain ill after treatment, and these patients are often referred to as having persistent or chronic Lyme disease (PLD/CLD),and it is estimated that nearly 2 million people in the US have PLD/CLD. PLD/CLD patients suffer from significantly impaired quality of life, utilize healthcare services more frequently and have greater limitations on their ability to work than the general population and patients with other chronic diseases. Neurological symptoms are common in PLD/CLD and have been linked to functional and structural changes in the brain. Very little, however, is known about the neurological manifestations of Lyme disease-contributing factors, diagnostic tools, or best treatments. Here, we analyze the MyLymeData patient registry, a project of LymeDisease.org, that contains over 18,000 patients with Lyme disease | sample, proc means | source | 7 |
| 180 | Transitioning from SAS to R | Ashley Tarasiewicz, Chelsea Dickens |
proc step, proc sort, proc transpose, if-then-else, adsl, adam dataset, proc freq, cdisc | source | 83 | |
| 181 | Translation from statistical to programming: effective communication between programmers and statisticians | Diana Avetisian | Statisticians and programmers who are working in clinical trials know that effective communication between the groups is a key factor for a study to succeed. But do we really know what statistician is expecting from programmers when they are working on complex statistical models and what programmers need to be efficient in the process of efficacy outputs creation? Programmers have a complex task when it is come to statistical analysis implementation, they are creating ADaMs based on CDISC standards which is allowing them to create outputs and on the next step producing TLFs to display results of statistical analysis. The truth is that it is not always obvious how data should look like to be suitable for a particular statistical analysis that's why programmers and statisticians are working together to help each other to achieve the best results. At the same time, statisticians have their own challenges | data science, raw data, proc step, adam dataset, clinical trial, data cleaning, data governance, cdisc | source | 10 |
| 182 | Two hats, one noggin: Perspectives on working as a developer and as a user of the admiral R package for creating ADaMs. | Benjamin Straub | R packages form a critical part of the R ecosystem by allowing users to gain additional scoped functions and documentation around common tasks. I have had the privilege of helping to develop the admiral R package, which seeks to allow users of R to build ADaM datasets. At the same time, I have had to use admiral in my studies for building ADaMs. In my presentation and paper, I will share perspectives on being a developer, a user, and the intersection of these two roles. As a developer, I will share what it is like to work on a cross-Pharma initiative developing software in a regulated environment. As a user, I will share what it is like to use admiral and R in my studies and issues that I have encountered. I will conclude with discussing the intersection of being both a user and developer and how it has be en a positive feedback loop on developing and enhancing functions and documentation in admiral | adam dataset, proc step, r package | source | 9 |
| 183 | Understanding Administrative Healthcare Datasets using SAS programming tools | Jayanth Iyengar | Changes in the healthcare industry have highlighted the importance of healthcare data. The volume of healthcare data collected by healthcare institutions, such as providers and insurance companies is massive and growing exponentially. SAS programmers need to understand the nuances and complexities of healthcare data structures to perform their responsibilities. There are various types and sources of administrative healthcare data, which include Healthcare Claims (Medicare, Commercial Insurance, & Pharmacy), Hospital Inpatient, and Hospital Outpatient. This training seminar will give attendees an overview and detailed explanation of the different types of healthcare data, and the SAS programming constructs to work with them. The workshop will engage attendees with a series of SAS exercises involving healthcare datasets | proc step, proc freq, proc sort, proc tabulate, insurer, sas programmer, health insurance, proc contents, proc format, proc sgplot, proc sql, deduplication, group by, service provider, sample, proc report, sas programming, by group processing, dataset, lookup, proc means | source | 25 |
| 184 | Unlock Your Greatness: Embracing the Power of Coaching | Priscilla Gathoni | As an individual, do you know your purpose in life? Do you understand your strengths, weaknesses, opportunities, and threats (SWOT) analysis? Do you know what you need to get to the next level in your career and life? Do you find it challenging to solve problems? Do you know how to be truly present in your conversations? Do you struggle to handle different personality types? What is your biggest frustration at work right now? Coaching is a vital tool for every individual to have at their disposal if they are committed to fulfilling and advancing their potential, as well as balancing life and work. Coaching empowers individuals to take action, think creatively, increase their personal performance and professional effectiveness in problem-solving and decision-making skills, and influence others. We will explore the value coaching brings to your life and work and why coaching should be part of your professional development plan | source | 9 | |
| 185 | Unraveling the Layers within Neural Networks: Designing Artificial and Convolutional Neural Networks for Classification and Regression Using Python-s Keras & TensorFlow | Ryan Paul Lafler, Anna T. K. Wade |
Capable of accepting and mapping complex relationships hidden within structured and unstructured data, Neural Networks are composed from layers of neurons with functions that interact, preserve, and exchange information between each other to develop highly flexible and robust predictive models. Neural Networks are versatile in their applications to real-world problems; capable of regression, classification, and generating entirely new data from existing data sources , Neural Networks are accelerating the breakthroughs in deep learning methodologies | neural network, data cleaning, regression analysis, training set, machine learning, unstructured data, deep learning, response variable, mae, data science, feature extraction, validation set, missing value, artificial intelligence, proc reg, structured data | source | 18 |
| 186 | Unravelling the SDTM Automation Process through the Utilization of SDTM Transformation Template | Lakshmi Mantha, Inbasakaran Ramesh |
Study Data Tabulation Model (SDTM) datasets play a pivotal role in supporting the submission of various study-related activities, including annual Developmental Safety Update reporting (DSUR), Periodic Safety Update Reporting (PSUR), Investigators Brochure (IB) and Clinical Study Report (CSR) deliverables. The automation of the SDTM process is gaining momentum, aligning with established Clinical Data Interchange Standards Consortium (CDISC) guidelines. However, the complexity arises due to variations in data collection standards across diff erent institutions. The efficient creation of SDTM datasets can significantly reduce the workload by saving numerous hours. In this paper, we will provide a comprehensive explanation of the SDTM transformation template, serving as a crucial input to the SDTM automation engine. This engine is designed to automate the SDTM program generation process, aiming to achieve at least a 70 percent automation rate | sdtm domain, raw data, clinical data, clinical trial, subject visits, dataset, adverse events, date variable, response variable, cdisc | source | 11 |
| 187 | Updates on Preparing a BIMO Data Package | Elizabeth Li, Carl Chesbrough, Inka Leprince |
Bioresearch Monitoring (BIMO) Program packages are used as part of the Food and Drug Administration (FDA) Center for Drug Evaluation and Research (CDER) submissions for the planning of BIMO inspections. The FDA has published a technical conformance guide to standardize the format for electronic submission of new drug application (NDA) or biologic license application (BLA) content. In a 2020 PharmaSUG paper, titled "Preparing A Successful BIMO Data Package", we presented a process of producing BIMO information, particularly the subject-level data line listings by clinical site (by-site listings) and the summary-level clinical site (CLINSITE) dataset, along with the reviewer's guide and define.xml for CLINSITE. Since then, the FDA has updated the technical conformance guide (TCG), https://www.fda.gov/media/85061/download, most recently on August 11, 2022 | excel, clinical data, treatment arm, clinical trial, label statement, adam dataset, adverse events, treatment effect, cdisc | source | 17 |
| 188 | Upholding Blinding in Clinical Trials: Strategies and Considerations for Minimizing Bias | Chen Li, Hong Wang, Ke Xiao |
In this approach, the independent statistical analysis team (iSAT) is responsible for unblinded analysis. Firewalls have been set up to ensure the unblind data access is limited to iSAT team: iSAT has no access to any ongoing study data except the snapshot data for the unblind analysis. Unblind data is saved and analyzed in secured folders for each study with access controlled by iSAT. After iSAT is unblinded, communication between iSAT and study team is minimized and documented if needed. The data with unblinding risk are transferred directly to iSAT. Planning considerations Preserving the blinding in clinical trials necessitates meticulous communication and strategic planning. The data logistics plan and results access plan must explicitly state the data transfer process and the individuals who can access the unblinded | source | 1 | |
| 189 | Utility Macros for Data Exploration of Clinical Libraries | Matt Maloney | Programmers are always looking for ways to simplify their work. A combination of laziness and creativity allows SAS users to leverage the SAS macro language to create their own tools to perform monotonous parts of data exploration. Clinical libraries often contain many datasets that need to be iterated over to perform certain data exploration tasks. This can be largely accomplished through the SAS macro language. This paper presents two of these utility macros. One is a macro that searches for any character match in SAS dataset names, dataset labels, variable names, variable labels, and variable values to quickly find information from a library of SAS datasets. The other is a macro that compares the datasets in two different SAS libraries for any discrepancies between datasets and performs simple value comparison | sas library, macro definition, proc compare, proc step, macro facility, dataset, label statement, macro call, proc sql, macro parameter | source | 14 |
| 190 | Utilizing Data Visualization for Continuous Safety and Efficacy Monitoring within Early Development | Margaret Wishart, Tamara Martin |
Early Development (ED) clinical research requires substantial ongoing data monitoring due to the complexity of patient safety, risk of drug toxicity, and variability of first-in-human (FIH) trials. Historically, clinical decisions were made throughout a drug development life cycle utilizing static statistical tables, data listings, and graphs. Though these outputs offer valuable statistical findings, the cadence of the data presentation is contingent on milestone deliveries. Within the industry, there is a shift to introduce interactive visualizations used for dynamic data review. Data Visualization (DV) is a solution to bridge the gap for the need of Near Real-Time (NRT) data review between major milestones to proactively empower informed data driven decision- making. Throughout this paper we will highlight the impact of a harmonious partnership cultivated by continued collaboration between DV and ED to allow for active safety and efficacy surveillance | clinical trial, clinical research, data review | source | 5 |
| 191 | Validating R for Pharma - Streamlining the Validation of Open-Source R Packages within Highly Regulated Pharmaceutical Work | Juliane Manitz, Anuja Das, Antal Martinecz, Jaxon Abercrombie, Doug Kelkhoff |
Healthcare regulators require documentation showing that the quality of the programming packages used for submissions was adequately assessed. This can pose a challenge when using R or other open-source software due to the varying development practices and quality expectations. This paper will highlight the R Validation Hub's risk assessment framework for R packages that has been utilized by various companies across the pharmaceutical industry. We showcase the products that working groups have developed: The {riskmetric} R package which evaluates the risk of an R package using a specified set of metrics and validation criteria. The {riskassessment} app which provides a dashboard to the {riskmetric} package with a Shiny app front end.The repositories workstream which develops a technical framework to maintain a 'repository' of R packages with accompanying evidence of their quality and the assessment criteria | data cleaning, pharmaceutical industry, risk classification, insurance risk score, r package | source | 6 |
| 192 | Variable Subset Codelist | Kang Xie | A codelist defines the controlled terminology of a variable within a data set. The controlled terminology provides the values required for submission to FDA, PMDA, or other regulatory agencies. For the standard codelist in an ADaM data set, we use the codelist in NCI/CDISC SDTM Terminology and ADaM Terminology. When the controlled terminology of a standard codelist contains more values than a variable possibly has, we can assign a subset codelist of this standard codelist to this variable. Furthermore, the controlled terminology of the codelist of a parameter-related variable is linked to either a specific parameter or a group of parameters within said variable, therefore, we can assign subset codelists to this variable based on parameters. This paper describes the approaches to assigning subset codelists to these two types of variables, aiming to refine their allowable values and acquiring extra information via the names of the subset codelists | define-xml, controlled terminology, response variable, cdisc | source | 7 |
| 193 | Versatile and efficient graphical multiple comparison procedures with {graphicalMCP} | Ethan Brockmann | Multiple comparison procedures (MCPs) are widely used in confirmatory clinical trials to control the probability of making false positive claims. Graphical approaches provide a flexible framework to accommodate commonly used MCPs and to create more powerful procedures. To allow both flexibility and efficiency, we've created graphicalMCP, an R-native package that is lightweight, performant, and educational. Using an extensible approach to allow different test types in different parts of a graph, the package covers all existing R packages for graphical approaches. Using vectorization and shortcuts, it greatly reduces simulation times compared to other similar packages, without using more efficient languages such as C++, which allows better compatibility with different computing environments. I t also has the capability to draw graphical MCPs using vertices and directed edges, giving users great visualization along with full quantitative details | clinical trial, type i error, significance level | source | 3 |
| 194 | Visual discovery in Risk-Based Monitoring using topological models | Kostiantyn Drach, Iryna Kotenko |
A strong endorsement from the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) for efficient oversight of clinical investigations leads to a crucial role of risk -based monitoring (RBM) in new drug development. The main goal of the RBM approach is to offer strategic and effective ways to allocate resources across a study based on several key indicators, such as data criticality, patient safety, protocol compliance, and others. One of the components in RBM is the Clinical Trial Site Monitoring which presents a significant challenge as it requires timely insights generation from the data coming from sites in almost real-time. As monitoring sites' activities is an important task to ensure protocol compliance and safety of patients and the resulting drugs, in this paper, we introduce how to aim it with a visualization approach using topological models of the data | dimensionality reduction, clinical trial, interval variable, proc princomp, correlation, manhattan distance, data science, wilcoxon test, proc freq, clinical research, euclidean distance, outlier | source | 31 |
| 195 | What Being a Peer-to-Peer Mentor Offers - Perspective from an Individual Project Level Contributor | LaNae Schaal | Typically, mentoring is done to help a person obtain skills to climb the career ladder. The traditional concept of mentoring is a structured relationship in which a senior individual imparts wisdom to a less experienced individual. For statistical programming, this could involve someone from a management level or a subject matter expert meeting with a more junior programmer. That hierarchical understanding overlooks a second possible structure for mentoring: peer-to-peer. Two individuals similarly situated in an organization committing to regular mentoring meetings. For example, a study level Senior Statistical Analyst serving as mentor to a Senior Statistical Programmer on a different study. During the last 7 years, I have participated in both types of mentoring relationships. This paper will briefly address components of effective peer-to-peer meetings. Then I will outline how being the mentor for peer-to-peer mentees has helped me develop professionally | clinical trial, statistical programmer | source | 3 |
| 196 | Win a PS5! How to Run and Compare Propensity Score Matching Performance Across Multiple Algorithms in Five Minutes or Less | Catherine Briggs, Sherrine Eid, Samiul Haque, S. Robert Collins |
With the increased use of real-world data (RWD) in clinical and healthcare settings, having a comprehensive comparison of propensity score matching (PSM) algorithms available to researchers is vital. In this paper, we will discuss different avenues for PSM and show the strengths and weaknesses of each using simulated data. We will cover aspects of performance of the algorithms from statistical measures to computing resources. The final part of the paper will demonstrate the effect of the matching algorithms on estimating the causal effect of treatment on an outcome. This paper will use matching algorithms from SAS, R, and Python and show their results through a SAS Viya Visual Analytics Dashboard | propensity score, propensity score matching, proc step, treatment effect, sas viya, proc ttest, treatment arm | source | 11 |
| 197 | Your Guide to Successfully Upversioning CDISC Standards | David Neubauer, Kapila Patel, Toral Patel, Soumya Rajesh |
As of early 2023, new versions of the CDISC standards (i.e., SDTM v1.7, SDTM IG v3.3, Define.xml v2.1) are either required or supported by the industry's regulatory agencies. This paper relays challenges and best practices the authors have experienced while up- versioning to these standards. Not all these practices are found in published standards. This paper will bring together the resources and lessons learned in one place, so that readers can skillfully navigate through the challenges of adopting these new standards. Highlights include strategies for dealing with value level metadata for variables with multiple codelist references, a new domain class, new domains, and domains referenced in TA UGs not seen in the IGs. We'll discuss best practices for data modeling: when to use new variables, supplemental qualifiers, and targeting the appropriate domains. We'll include experiences interpreting and dispositioning validation output from the applicable conformance rules | sdtm domain, data cleaning, adverse events, clinical trial, where statement, data validation rules, controlled terminology, cdisc | source | 19 |
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