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BUGSPharmaSUG 2026 Papers - Index
245 papers. Source: PharmaSUG (Pharmaceutical Software Users Group) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | %Compare_counts: A Macro for Speeding Up the QC Process When Proc Compare Slows it Down | Michael Garside | Quality control (QC) is a critical component of clinical trial programming and is necessary to ensure the accuracy and consistency of SDTM, ADaM, and output data sets. Current tools (such as PROC COMPARE) are useful and necessary. However, they can be inefficient in certain scenarios. This paper introduces the %Compare_counts SAS macro, a macro designed to directly compare the frequency of value combinations across specified variables between production and QC data sets. As a complementary QC tool, %Compare_counts provides a fast and effective approach for pinpointing discrepancies, thereby streamlining the QC process |
proc compare, proc step, macro call, proc freq, macro variable, cdisc | source | 6 |
| 2 | A Fantasy in Three with PROC FCMP: Memoization of Resource-Intensive Calculations, in-Memory Hash-Object Storage and Retrieval Operations, and Disk-Based Persistent Data Set Modification and Preservation | Troy Martin Hughes | The SAS Function Compiler procedure ( i.e., PROC FCMP) empowers SAS practitioners to design user - defined functions and subroutines that make software more maintainable, flexible, configurable, readable, and reusable. The SAS hash object, a built-in data structure and type of associative array, stores values in key-value pairs, and facilitates fast and efficient in-memory lookup operations that store and retrieve one or more values. Combining hash horsepower and FCMP finesse yields a function that is both powerful and pretty while abstracting ( and hiding) complexity inside the function definition. Because of their key -value structure, hash objects commonly operationalize memoization-the key -based caching of resource - intensive results so they do not need to be recalculated in the future |
hash table, proc fcmp, dataset, conditional do loop, put statement, array, proc step | source | 11 |
| 3 | A Faster Algorithm for the Finkelstein-Schoenfeld Test and Win Ratio in Hierarchical Composite Endpoint Analysis | James Austrow | More than 95% of the runtime of your next power analysis simulation could be eliminated. We overturn the previously-unchallenged assumption that the win ratio requires explicit comparison of every pair of subjects by presenting a novel, sub-quadratic algorithm. We also demonstrate empirical speedup against incumbent software of 20-50x for mid-size trials, and over 100x for very large trials. Hierarchical composite endpoints, and the win ratio in particular, have been growing in popularity over the conventional composite endpoint due to their ability to prioritize outcomes by clinical importance. However, the computational expense of these methods has long been recognized as a weakness, and various workarounds have been proposed. Our technique is the first to directly confront the fundamental scaling problem. We present the algorithm, a proof of correctness, and an implementation in pure Python | power analysis, adverse events | source | 13 |
| 4 | A fully automated PDF solution using SAS without third-party PDF tools | Zhongan Chen | In clinical trials, statistical programmers often produce well-bookmarked PDF packages of TFLs (Tables, figures and listings) for review or submission purposes. Traditional ways often include a manual process, using Adobe Acrobat, or other third-party PDF tools (Python, Libreoffice, Sejda etc.) to convert RTF to PDF and then combine PDF files into a package. They often come with extra cost in software licenses and could be time-consuming. This paper outlines a novel, free, fast and fully automated approach to generate bookmarked PDF packages with TOCs (Table of Content) using SAS. The only software needed other than SAS is Microsoft Word which should be available to most users already. No third-party PDF tools, such as Adobe Acrobat is needed in this process. Everything is done with SAS macros and is fully automated | dataset, out=, sample, analysis set | source | 10 |
| 5 | A Governed Git Workflow Using Azure Repos for GxP Compliant Statistical Programming | Jing Yu | Effective version control is essential for collaborative statistical programming, reproducible analyses, and validated deliverables in GxP-regulated pharmaceutical environments. This paper describes a standardized Git-based workflow implemented at a large pharmaceutical company that uses Azure DevOps (Azure Repos) as the repository for SAS and R programming assets. Azure DevOps provides integrated capabilities for code collaboration, automated builds and tests, and traceable deployment pipelines . The workflow leverages Git's distributed development model while enforcing centralized governance through standardized repository structures, protected branches, pull -request-based peer review, and automated quality checks. Developers work locally using familiar tools such as SAS Enterprise Guide, RStudio, or Visual Studio Code, performing regular pull and push operations to synchronize with Azure Repos | clinical programming, sas enterprise guide, audit trail | source | 8 |
| 6 | A Human-in-the-Loop AI-Assisted Framework for ADaM Standardization | Chunqiu Xia, Feiyang Du |
Standardized ADaM (Analysis Data Model) implementations play a critical role in ensuring consistency, traceability, and regulatory compliance in clinical trial analysis. When organizations acquire clinical studies from external companies, statistical programming teams often receive ADaM SAS code and specifications developed under different programming standards and conventions. These externally developed deliverables typically require substantial manual rework before teams can integrate them into internal workflows and reuse standard Tables, Listings, and Figures (TLF) SAS programs. This paper explores a human-controlled, template-driven framework that integrates large language model assistance, via the LLM API, into the ADaM standardization workflow for acquired studies | data cleaning, adam dataset, clinical programming, data pipeline, adverse events, clinical trial, cdisc | source | 12 |
| 7 | A Map to Success with Data Visualization Using ODS Statistical Graphics | Richann J. Watson, Louise S. Hadden |
Creating custom graphics does not have to be a daunting experience. Anyone who has produced a graph using Output Delivery System (ODS) Graphics has unknowingly used the Graph Template Language (GTL). We take you on a guided tour of how to create a truly custom graph. Our first stop starts with an illustration of a basic plot with little complexity produced with Statistical Graphics (SG) procedures. We then make a pit stop with the TMPLOUT option to help convert the simple plot to GTL. On our road to create a custom graph we need to get out our map to build a map. Our last stop of this adventure takes us to the combining of these two graphs to illustrate the power of GTL to truly customize your graphs |
proc step, bar chart, proc sgplot, sample, proc template, graph template language, scatter plot | source | 23 |
| 8 | A Novel Approach to Inter-Collaboration using IDEs and GitHub | Sydney Hyde, Tamara Martin |
In the pharmaceutical industry, specific programming languages have long been the dominant standard. However, open-source technologies have seen increasing adoption, driven by active user communities, and rapid innovation. As a result, new graduates entering the industry bring increasingly diverse programming backgrounds. In a fast-paced environment, it is critical to leverage both existing expertise and the skills of the next generation of programmers. This goal is often hindered by limited overlap in programming language experience across teams, which can constrain projects to a subset of available talent and lead to duplicated effort. This paper presents a collaborative system, built on existing platforms, that enables cross-language collaboration. Using practical examples, we demonstrate how combining GitHub repositories with modern interactive development environments (IDEs) enables seamless collaborative workflows | data cleaning, pharmaceutical industry | source | 8 |
| 9 | A Practical Approach to Multiple-Period CLINSITE Preparation | Liyuan Huang, Amanda Plaisted, Sreedhar Bodepudi |
The preparation of the summary-level clinical site dataset (e.g., CLINSITE) for a Bioresearch Monitoring (BIMO) package in an NDA submission requires consistent, traceable, and regulatory -compliant data structuring across study periods. In the study described, two periods were included: a double- blind (DB) treatment period, which contained two primary efficacy endpoints and safety evaluations for two different population sets, and an open-label extension (OLE) period, which focused exclusively on safety assessments at the time of the primary analysis database lock. To maintain consistency in data review and downstream validation without introducing any variable that is not in the FDA BIMO updated technical conformance guide (TCG, Version 3.0, August 11, 2022), the variable STUDYID in CLINSITE was structured to incorporate both DB and OLE data | primary endpoint, data cleaning, analysis set | source | 6 |
| 10 | A Practical Roadmap for Modernizing Legacy Clinical Applications | David L. Ward | Many pharmaceutical organizations depend on long-st anding analytical applications that are increasingly difficult to maintain, validate, and extend. While these systems often remain mission-critical, accumu lated technical debt can hinder productivity, increase operational risk, and limit the adoption of modern analytical techniques. This paper presents a practical, incremental roadmap for modernizing legacy clinical applications while maintaining continuity with existing SAS-based and regulated workflows. The discussion begins with guidance on project plan ning and assessing modernization readiness, then moves to strategies for tool and language selection . Attendees will gain a clearer understanding of how alternative products and open-source languages can be evaluated and introduced alongside established platforms, minimizing disruption and regulatory risk | data cleaning, operational risk, data lineage, regulatory submission | source | 15 |
| 11 | A Practical Roadmap for the 2026 Enterprise Generative AI Stack: AI Agent Architectures, Frameworks, and Secure Deployment | Ryan Paul Lafler | Generative AI is rapidly reshaping how organizations search, reason over, and contextualize information, yet deploying these systems on private, confidential, and sensitive data introduces distinct architectural, performance, and governance challenge s. This paper presents a practical roadmap for the 2026 enterprise generative AI stack, with emphasis on agentic AI architectures built on retrieval -augmented generation (RAG), vector embeddings, and secure model deployment. Core concepts including encoding, similarity search, and vector databases are introduced to explain how knowledge is stored, retrieved, and reused across AI agents, alongside API-driven patterns that enable tools for search, reasoning, and contextualization | life sciences, analytics platform, feature selection, data cleaning | source | 8 |
| 12 | A specification-driven approach to improve the reliability of AI-generated SDTM transformation programs | Rostislav Markov | Clinical data transformation pipelines are increasingly augmented with artificial intelligence (AI)-assisted code generation. However, in regulated environments, the uncontrolled use of AI can introduce unacceptable risks, including incorrect transformations, inconsistent traceability, and increased validation burden. This paper presents a practical, specification-driven approach to governing AI-assisted function development using software development lifecycle (SDLC) artifacts including Behavior-Driven Development (BDD) specifications, test contracts, and project conventions. We evaluate how different levels of AI prompt governance affect the correctness and integration quality of generated transformation functions. Using eight production functions from an SDTM transformation framework, we compare loosely specified AI prompts against increasingly governed prompts grounded in BDD specifications, structural conventions, and test scripts | data cleaning, clinical data, data pipeline, error message, sample, exact, proc sql, cdisc | source | 6 |
| 13 | A Standardized R Graph Library for Production-Ready Analysis Figures | Chunting Zheng, Margaret Huang, Xindai Hu |
Production of Clinical Study Report (CSR) figures requires strict adherence to internal standards, including exact specifications for fonts, layouts, and aesthetics. Traditionally, programmers implement similar code blocks for each new figure, leading to redundancy, inefficiency, and an increased risk of inconsistency. To address this challenge, we developed a comprehensive R graph library that automates the creation of actual analysis figures using standardized templates for common graph types such as scatter, bar, box, swimmer, line, spaghetti, forest and Kaplan-Meier plots. Each template encapsulates a uniform structure and theme, ensuring consistency across projects while minimizing the need to manually reproduce code. At the same time, flexibility is preserved through high - level parameters (e.g., point size, opacity, text size, model type, formula) and customized arguments that allow additional layers for plot-specific customization | change from baseline, forest plot, raw data, ls mean difference, bar chart, least squares mean, standard deviation, proc means | source | 19 |
| 14 | A Statistical Programmer-s Guide to Tipping Point Analysis in SAS | Ang Xu | Missing data due to treatment discontinuation poses challenges for longitudinal clinical trial analyses, as primary analyses often rely on unverifiable assumptions such as follow -the-reference (FTR). Sensitivity analyses are therefore necessary to assess robustness of conclusions to departures from these assumptions. This paper presents a practical implementation of tipping point analysis within a multiple imputation framework using SAS. Non-monotone missing data are first transformed into a monotone structure, followed by reference-based multiple imputation and systematic delta adjustments to represent post-discontinuation deviations from FTR. Adjustment parameters are applied by treatment group and scaled by time since discontinuation. Each completed data set is analyzed using the primary model, with results combined using standard multiple-imputation inference | imputation, multiple imputation, sample, treatment effect, missing value, sensitivity analysis, clinical trial, regression analysis | source | 9 |
| 15 | A systematic approach for imputing missing dose information in population pharmacokinetic analysis datasets | Prema Sukumar, Renuka Hegde, Erin Dombrowsky, Neelima Thanneer |
Accurate recording of dosing events is essential for population pharmacokinetic (popPK) analysis. Missing dose dates or times frequently occur due to limitations in case report forms or incomplete data reconciliation, potentially compromising data integrity and analysis quality. Standardized imputation rules have been developed for oral, intravenous (IV), and subcutaneous (SC) doses to ensure complete dosing histories for popPK datasets. Dose records lacking both start and end dates are excluded. Oral, IV, and SC studies use PK collection and adjacent dosing times for time imputation. IV and SC studies also use protocol-defined durations for imputation when dates or times are missing. ADDL, II are also used in Oral studies for missing dose records. All imputed records are flagged, and the algorithm is documented in both dataset specifications and pharmacometric reports | imputation, sample, population pk | source | 7 |
| 16 | Accelerating CDISC SEND Conversion with AI: From Raw Preclinical Data to Regulatory-Ready Datasets | Nattawit Pewngam, Chotika Chatgasem, Titipat Achakulvisut |
The Standard for Exchange of Nonclinical Data (SEND), developed by the Clinical Data Interchange Standards Consortium (CDISC) , defines the standardized structure and format for submitting nonclinical study data to regulatory authorities. Converting extensive unstructured study materials, often consisting of reports, tables, and scanned documents, into SEND -compliant data sets remains a manual, error-prone, and time-consuming process that relies on repetitive data entry. This inefficiency reduces consistency, traceability, and overall regulatory readiness. Here, we introduce CDISC-SEND Conversion platform, an automated framework that integrates large language models (LLMs) with retrieval-augmented generation (RAG), to streamline and standardize this data transformation. Our platform rapidly normalizes and maps unstructured study content into SEND structures defined in the SEND Implementation Guide (SENDIG v3.1.1) | controlled terminology, data cleaning, data standardization, regulatory submission, cdisc | source | 10 |
| 17 | Accelerating Code Translation and Generation with ChatGPT | Brian K. Varney, David J. Bosak |
Recent years have seen an explosion of use cases for generative AI. People are using AI to do research, write papers, summarize articles, and write code. At first, the quality of the output was not very good. Yet with each new release, the output is getting better and better. It is now feasible to perform tasks that previously would not be considered. In this paper, we will explore the topic of code translation and generation involving SAS & R using ChatGPT. The paper will first provide an overview of how to perform such a translation. We will then elaborate on a list of tips and tricks to make the machine translation better. Finally, we will point out some limitations of the technology and tell you what to watch out for. The paper will hopefully present you with enough information to try translating some code yourself | proc freq, proc report | source | 11 |
| 18 | Accelerating Open-Source AI with AWS Bedrock: Architecting LLM Integration with Posit Workbench & Positron | Philip Bowsher | A challenging aspect of using AI in statistical programming is just having access to foundational models within organizations. Curating and fine-tuning local models can be labor intensive and many Pharmaceutical organizations limit access to public foundational models for various reasons such as sensitivity of clinical data. This paper will discuss Amazon Bedrock as a managed service to governed and enterprise-grade access to foundation models. This paper will break down what Bedrock is, how it works and its implications in late-stage reporting. Below will introduce Bedrock and then break it down into use-cases for users and administrators | clinical data, clinical programming | source | 6 |
| 19 | aCRF In Full: A Complete Solution for Relatively Little Work | Carlo Radovsky | Leveraging Python and SAS, users are able to extract, analyze, and apply PDF comment annotations in accordance with CDISC conventions. Past solutions were a mix of manual and automated processes, often relying on the inconsistent and limited representation of the annotations in FDF or xFDF formats. By leveraging Python and its PDF-related libraries, a far more robust and flexible approach is possible. The walkthrough will also explore the pros and cons of using AI to help build the code to support the process, and will detail how to support a fully submission-ready aCRF document including annotations, bookmarks, and a page reference in support of Define-XML integration | sample, excel, cdisc | source | 4 |
| 20 | ADaM Pet Peeves Part 2: More Things Programmers Do That Make Us Crazy | Sandra Minjoe, Nancy Brucken, Alyssa Wittle, Nate Freimark, Richann J. Watson, Paul K. Slagle, Tatiana Sotingco |
The authors have been actively involved in the Clinical Data Interchange Standards Consortium (CDISC) Analysis Data Model (ADaM) team for many years, and they include past and future CDISC ADaM Team Leads, CDISC ADaM Sub-team leads, and authorized CDISC ADaM trainers. Because of our extensive ADaM expertise, we each end up reviewing a lot of ADaM submissions before they are sent to regulatory agencies. Following the companion paper presented at PharmaSUG in 2025, this paper highlights additional common issues that we've seen ADaM developers make. For each topic, we explain the issue and provide a better and/or more conformant approach | adam dataset, sample, sdtm domain, baseline, clinical trial, cdisc | source | 15 |
| 21 | Advanced SAS Graph Template Language (GTL) with Practical Examples from Oncology Trials | Ceng Qian, Xu Wen, Wei Lei, Ling Han |
With increasing complexity of clinical trials, the business needs for high-dimensional and dynamic data visualization become more crucial to help decision-making and drug development. Compared to traditional SAS SG procedures, SAS Graph Template Language (GTL) provides more user-defined features and flexibility. In this paper, we illustrated several practical examples of customized figures using advanced SAS GTL code, including multi-layer waterfall plots, data-driven heatmaps, longitudinal swimmer plots, and cycle-based adverse event (AE) incidence plots from Oncology trials. The purpose of this paper is to motivate SAS programmers to utilize the powerful GTL and create high quality graphs | waterfall chart, adverse events, study day, best response, swimmer plot, graph template language, proc step, advanced sas, proc template, concomitant medications | source | 15 |
| 22 | Agentic R in Clinical Trials: Empowering Statistical Programmers with Open Source LLM Packages & Positron Tools | Philip Bowsher | Enterprises are increasingly adopting Positron for its AI capabilities. Within, users can use Claude Code to run agentic workflows. This paper will introduce these tools and a simple proof of concept with the pharmaverse | data cleaning, clinical trial | source | 15 |
| 23 | AI-Assisted Modular Design for R Markdown Report Generation A Hybrid Architecture for Enhanced Maintainability and Cross-Study Scalability | Song Liu, Jingwei Xiong |
Automated report generation systems for scientific study reports in R Markdown often begin as sequential codebases that become increasingly difficult to maintain and adapt as complexity grows. This paper presents a comprehensive methodology for designing A I-friendly modular architecture using Chemistry, Manufacturing, and Controls (CMC) Process Characterization (PC) study report as a representative case study to illustrate the design principles, implementation patterns, and architectural decisions. The proposed approach employs hybrid architecture combining child R Markdown documents with functional programming patterns and a centralized configuration object passed explicitly through all layers of the system to create modular components. The function layer, organized into paragraph orchestrators and sentence- level functions, encapsulates all computational logic and narrative assembly, ensuring a clean separation between content generation and document rendering | data wrangling, main effect, response variable, data science | source | 13 |
| 24 | AI-Augmented SDTM Review: A Practical Framework Enhanced by a Structured Prompt Library | Vihar Patel | SDTM review is foundational to clinical trial data quality and regulatory readiness. Historically, this has been a manual and time-intensive process involving extensive cross-checking of CRFs, mapping specifications, the Protocol, SAP, and SDTM datasets themselves. As the volume and complexity of clinical data grows, conventional review approaches struggle to scale effectively. Recent advancements in AI, particularly Large Language Models (LLMs), offer a new paradigm: AI- Augmented SDTM Review, where AI assists by performing structured data checks, semantic reasoning, document alignment, and anomaly detection while human programmers retain responsibility for adjudication and interpretation. This aligns with emerging regulatory expectations on responsible AI use, which emphasize human-centric design, clear context of use, transparency, and governance when AI supports data used in regulatory decision-making | data cleaning, sdtm domain, clinical data, clinical trial, cdisc | source | 10 |
| 25 | AI-Driven Intelligent Platforms for ADaM Specification and Code: Empowering Clinical Data Analysis | Tingting Zeng, Jia He, Yaohui Zhu, Shuang Gao, Jinling Li, Nana Yang |
Traditional ADaM (Analysis Data Model) implementations encounter critical challenges, including excessive complexity, time constraints, fragmented cross-layer traceability, and difficulty managing changes under evolving analysis requirements. To address these challenges, we have developed SpecMaster (ADaM Specification Management) and ACIRA (ADaM Code Intelligence for Rapid Analysis), two AI-driven platforms that transform clinical data analysis workflows through integrated specification management, code generation, and metadata intelligence. The framework combines centralized knowledge resources with multi-agent AI collaboration, end-to-end metadata lineage, and human-in-the- loop oversight. This design enhances efficiency, consistency, and cross-study governance while ensuring human analysts retain oversight of interpretation, validation, and final decision-making | clinical data, data cleaning, data science, adam dataset, sdtm domain, cdisc | source | 11 |
| 26 | AI-Enhanced R Shiny App for Real-Time Clinical TLF Coding and Preview | Dickson Wanjau | In this paper, we present an interactive R Shiny application that streamlines the creation, preview and refinement of RTF-based TLFs for clinical reporting using the open-source "r2rtf" R package. The application merges a live code editor with a real-time PDF preview, enabling users to iteratively write or modify code and immediately preview results. Users begin by uploading "table-ready" datasets in various formats e.g. CSV, EXCEL, SAS (.xpt or .sas7bdat formats), R file formats (.RData, .rda) etc. The app previews data, offers column filtering, and auto-generates starter r2rtf code which users can refine in a shinyAce editor. Upon code submission, the app executes the user's script in a controlled environment, updates the preview with a rendered PDF and logs any warnings or errors for debugging. To support adoption and learning, we have embedded a retrieval-augmented generation (RAG) AI chatbot trained specifically on the "r2rtf" package documentation |
clinical programming, excel, clinical trial, data cleaning | source | 15 |
| 27 | AI-First Leadership in Biometrics: Redefining Strategy, Teams and Execution in the Agentic AI Era | Kevin Lee | As AI evolves from passive tools to autonomous, goal-driven systems, Biometrics faces a fundamental leadership inflection point. Agentic AI, AI Agents capable of planning, reasoning, executing tasks, and collaborating with humans, require Biometrics leaders to move beyond AI adoption toward AI-first leadership, where AI actively participates in how work is designed and delivered. The presentation reframes AI in biometrics from single-use Gen AI applications to Agentic workflows that orchestrate complex tasks. Through practical examples, including statistical programming, exploratory data analysis, clinical documentation development, quality control, and cross-functional coordination, the presentation demonstrates how Agentic AI can function as a digital team member that initiates tasks, manages dependencies, and escalates decisions while preserving human scientific oversight. The presentation introduces an AI-first leadership framework structured around system, process, and people | data cleaning, quality control, clinical research, sdtm domain, clinical programming, clinical data, cdisc | source | 7 |
| 28 | AI-Powered Multiple-Agent Pipeline for Automating ADaM Dataset Generation | Lucas Liu, Bo Ci |
Preparation of Analysis Data Model (ADaM) datasets remains one of the most time- and resource- intensive activities in clinical trial programming. Although Clinical Data Interchange Standards Consortium (CDISC) standards provide structural consistency, ADaM development still depends heavily on it erative manual coding, debugging, and repetitive quality control practices. Trial-specific derivations and ambiguous specifications further limit automation. This paper presents an AI-powered, multi-agent pipeline that automates the end-to-end ADaM derivation workflow under human supervision. The framework begins with AI-driven specification review to identify ambiguous language, implicit logic, and missing edge-case handling. After user confirmation, a code generation agent produces executable programs aligned with the finalized specification. Generated code is executed in a sandbox environment with access to Study Data Tabulation Model (SDTM) datasets | adam dataset, quality control, data cleaning, error message, cdisc | source | 5 |
| 29 | AI-Recommended Color Palettes with QC for R Shiny Figures | Yi Guo | In the pharmaceutical industry, the safe and controlled use of artificial intelligence (AI) remains an active topic of discussion. If AI is viewed as a programmer, concerns often focus on unforeseen bugs and outputs. When viewed as an artist, AI can offer unexpected inspiration and expand creative possibilities. In this paper, we use AI as an artist-inspired tool to extend the dynamic color selection feature of an existing R Shiny figures application. Using the {openai} package, the app calls a large language model (LLM) via an API to generate candidate color palettes based on user-defined themes. Within the app, each subgroup is assigned a unique color picker with a default color and an option for manual adjustment, while natural language prompts are used to guide the LLM in generating candidate color palettes for subgroup-level visualization. However, this alone is not sufficient, as an artist's aesthetic choices may not always meet regulatory submission requirements | clinical trial, ods json | source | 19 |
| 30 | Algorithms to align the distribution of follow-up across independently collected cohorts when comparing time to event endpoints using conventional Kaplan Meier and Cox regression methods. | Shankar Srinivasan, Yvonne Buttkewitz, Regina Uttenreuther, Baldeep Chani Talwar, Manjari Dissanayake |
Using data from two independently collected cohorts, such as a historical control from a completed randomized trial and a 1:1 propensity score matched treated cohort from a prospective single arm trial, one can often have differences in the accrual time and follow-up post accrual. Such a context requires special approaches for time-to-event endpoints. We will describe an algorithm and provide SAS code to implement the algorithm together with its rationale, focusing more on control data. In our analyses, we matched cohorts when the enrollment of our prospective cohort was completed and before the availability of complete outcome data in the prospective cohort. A team blinded to outcome data from both cohorts conducted the matching. We then tallied events across cohorts while our trial was ongoing till the event thresholds for analyses were reached | data cutoff, raw data, proportional hazards assumption, randomization, sample, primary endpoint, time-to-event endpoint, data science | source | 12 |
| 31 | An Agentic AI Framework That Reads Statistical Analysis Plans and Generates TFL Table of Contents | Siqi Wang, Toshio Kimura, Songgu Xie, Weiming Du |
What if AI can read and understand the SAP and learn about endpoints, analysis sets and analysis | data standard, data science, analysis set, secondary endpoint | source | 6 |
| 32 | An Alternative Option to Create XPT Files with a SAS Function | Jose Hernandez Rivero, Ruth Marisol Rivera Barragan |
Creating SAS transport (XPT) files remains a critical step in preparing data sets for clinical and regulatory submissions. Programmers still face the long-standing limitations of PROC COPY, PROC CPORT, and DATA STEP, all bound by the SAS Version 5 format restricting variable names to eight characters and labels to forty. These constraints are reasonable when data sets are strictly CDISC-compliant, but in many real-world scenarios -such as raw data transfers, EDC extracts, medical device studies, or sponsor-specific requests- they become impractical and even counterproductive. Recognizing this gap, we explored the LOC2XPT SAS function as a modern alternative that enables greater flexibility while maintaining submission integrity. LOC2XPT generates fully functional XPT files without the usual truncation or metadata loss, preserving variable and label names exactly as intended |
proc step, proc copy, proc cport, label statement, cpu time, regulatory submission, data cleaning, cdisc | source | 5 |
| 33 | An Approach for Generating Tumor Biopsy Datasets for Drug Development | Ryan Hernandez-Cancela, Jeff Cheng, Sandeep Meesala |
Drugs that target tumor-associated antigens (TAAs) represent an important domain within oncology, due in part to their proven clinical benefit. To support in- house development of these drugs, certain exploratory analyses were planned. These analyses will help identify potential TAA targets, and they require data from tumor biopsies collected in a study . However, c omprehensive data sets that include all pertinent biopsy data were not available. Methods were needed for generating these data sets in an accurate, robust, and timely manner. Therefore, a suite of tools was developed for creating biopsy data sets. An algorithm was designed to match biopsies to clinical data from a specific CRF. Programs were written in SAS to derive the values of certain CDISC variables (e. g. smoking status) at the biopsy date. Supportive documents , such as data set specifications and issue logs, were created in EXCEL to provide traceability and help track issues |
clinical data, excel, data cleaning, cdisc | source | 10 |
| 34 | An End-to-End R-Based Pharmacokinetics (PK) Workflow for Regulatory Submission: The INAVO120 Study | Qi Liu, Shweta Vadhavkar |
The INAVO120 clinical study supported the development and regulatory approval of inavolisib and represented Roche/Genentech's first end-to-end regulatory submission executed entirely in R using the OCEAN (One CEntralised Analytics), an open-source, cloud-enabled analytics environment. This work emphasizes modern programming practices for clinical pharmacology analyses, including pharmacokinetics (PK), pharmacodynamics (PD), and model-informed drug development. The Clinical Pharmacology, Modeling and Simulation Analyst (MSA) team, in collaboration with Pharmaceutical Development Data Sciences (PDDS) and OCEAN platform teams, redesigned the clinical study reporting process by transitioning PK analysis datasets, Analysis Data Model (ADaM) deliverables, Tables, Listings, and Graphs (TLGs), and Modeling & Simulation outputs from SAS to R | regulatory submission, adam dataset, data cleaning, analytics platform, clinical trial, clinical research, key, clinical data, compartmental analysis, pk parameter, data science, cdisc | source | 5 |
| 35 | An Experience Using R, SASSY and Tidyverse For Clinical Trial Analysis, From A SAS Programmer Perspective | David W. Franklin | In recent years there has been a move to at the very least consider doing some of the clinical trial submission analysis using other software packages, for example R. Some groups have looked at SDTM and/or ADaM and some have looked at speciality tables, listings and graphs. Few have done a complete package. This paper looks at using R, tidyverse and sassy and putting raw data into SDTM and ADaM datasets, then using this data to create tables, a listing and a figure as one software solution. Along the way we will look at some techniques using these three packages to produce the outputs as .rtf and .xpt, read .xpt and .CSV formatted data files, and look at challenges including dates and times |
if-then-else, label statement, proc step, clinical trial, character variable, adverse events, raw data, sas programmer, vital signs, data step, dataset, proc freq, cdisc | source | 27 |
| 36 | An Innovative solution for Interactive Dashboards Using Python Flask Framework for Clinical Data Analytics | Manish Bhagchandani | Traditional clinical reporting tools and built -in dashboard solutions often have fixed structures and limited flexibility, which can restrict deeper exploration of clinical trial data. This paper demonstrates how the Python Flask framework can be used to b uild interactive and customizable dashboards using SDTM and ADaM datasets. By moving away from rigid, pre -packaged tools to a lightweight web -based framework, Flask enables greater control over layout, logic, and user interaction while integrating smoothly with the Pharmaverse ecosystem. An end -to-end workflow is presented, including data preparation using Pandas, interactive visualizations created with Plotly, and enhanced user interactions using JavaScript. These dashboards support data validation, study review, safety monitoring, and exploratory data analysis by statistical programmers, clinical scientists, and study teams. They promote effective collaboration by providing timely and easy-to-interpret views of clinical data | clinical data, clinical trial, line chart, adverse events, data review, cdisc | source | 6 |
| 37 | Analysis Concepts role within the CDISC 360i vision | Alyssa M. Wittle, Brian Harris |
Since January 2025, a CDISC working group has been working on defining a model for Analysis Concepts to address a gap in enabling the end-to-end linking, automation, and interoperability envisioned by the CDISC 360i initiative. Just as biomedical concepts (BC) serve as an essential semantic framework that connects and aligns various foundational standards in clinical research, analysis concepts can provide a similar semantic framework that not only extends into analysis and reporting components but also ensures a clear link to the protocol (as represented by the Unified Study Definition Model (USDM)) as well as the existing BC library | data science, clinical research, cdisc | source | 5 |
| 38 | Applications of PROC COMPARE to Parallel Programming and other projects | Jayanth Iyengar | PROC COMPARE is a valuable BASE SAS procedure which is used heavily in the Pharma industry and other areas. By default, the capability of PROC COMPARE is to reconcile two data sets to determine if they have equivalent sets of records and sets of variables. In the clinical field and elsewhere, PROC COMPARE is often used to validate data sets in projects which involve parallel programming, where programmers independently perform the same tasks. In this paper, I will discuss the role PROC COMPARE plays in different SAS tasks, including DATA STEP merges, parallel programming, generation data sets, and more |
proc compare, proc step, dataset, sas log, missing value, sas output, numeric variable, proc sql, length statement, sas base, master data management, data cleaning | source | 21 |
| 39 | Appreciating PROC SUMMARY/MEANS in Many NWAYS vs Summary Function in R | Brian K. Varney | The summary/MEANS procedure in SAS is a very powerful and flexible procedure for summarizing continuous analysis variables and returning simple summary statistics. This paper intends to examine what happens when the NWAY option is left off PROC SUMMARY in detail, the problems that can arise when you have many class variables. We will examine a SAS Macro that mimics the PROC SUMMARY without the NWAY option. We will also compare the same approach in R using the Tidyverse summary function |
proc means, proc step, class variable, macro definition, macro loop | source | 8 |
| 40 | Approaches Integrating SAS LSAF and Pinnacle 21 Enterprise for SDTM/ADaM Dataset Validation | James Zhao, Joshua Lin |
Early and continuous validation of clinical trial datasets is essential for ensuring regulatory compliance, improving data quality, and reducing late-stage rework. While industry practice has traditionally focused on validating transport (XPT) files immediately prior to submission, this approach delays the identification of structural and standards-related issues. This paper describes enterprise-grade approaches for integrating SAS Life Science Analytics Framework (LSAF) with Pinnacle 21 Enterprise to enable automated validation of SAS XPT datasets throughout SDTM and ADaM development | data cleaning, excel, cdisc | source | 8 |
| 41 | ARMed AutoTable Macro Agents: An ARM-Driven Framework for Automated Analysis Table Generation | Chengxin Li | This paper presents AutoTable , a metadata -driven automation framework implemented entirely in SAS macros, where macro parameters are directly aligned with Analysis Results Metadata (ARM) framework. The system focuses on efficient, reproducible generation of non-inferential analysis tables while integrating seamlessly into existing statistical programming workflows. Four core macro agents -%ADSLEVL, %BDSSTAT, %BDSSHIFT, and %OCCFREQ -support major categories of non-inferential analyses. For each macro invocation, the framework automatically generates: Formatted table output (RTF, PDF, or XLSX) Plain, executable, submission-ready SAS code Final results dataset (RDS) Aggregated ARM dataset (ARMDS) to facilitate define-xml generation * Execution log file By leveraging ARM as both specification and execution driver, AutoTable captures core structural features and layout patterns of table shells without requiring table shell standardization or digitization as prerequisites | macro call, shift table, clinical programming, change from baseline, adam dataset, demographics table, proc freq, clinical trial, macro parameter, cdisc | source | 10 |
| 42 | Assessing Quality of Real-World Data Sources | S. Robert Collins | The FDA continues to update guidance on the use of real-world data (RWD) and evidence (RWE) for drug and biologic products and medical devices. While regulators are increasingly open to the use of RWD sources that have flourished as a result of technical advancements and requirements such as medical data interoperability, sponsors and regulators believe there are still significant opportunities for the use of RWD as long as the quality and provenance of the sources are demonstrable. In this session, we present a discussion applicable to anyone interested in the use of RWE of "first - person" and "third-person views" of data by the generating institutions and the consumers of pooled data and establish that source-based quality methodologies have the greatest potential for improved data. We next review the factors that give rise to problematic data at the source. We then look at practical approaches for a third-party data consumer to assess and document data quality | data cleaning, exploratory data analysis, data science, record count, sample, insurance claims | source | 4 |
| 43 | Authentic Leadership for SAS Programming Leaders | Lisa A. Mendez | Leading a team of SAS programmers isn't just about knowing your PROC SQL from your DATA steps . It's about showing up as a real human. In this paper, I'll share my take on authentic leadership in technical environments, where precision, autonomy, and complexity often dominate the landscape. Authentic leadership, to me, means leading as yourself, communicating transparently, and listening like it actually matters. We'll explore how these three attributes- "being yourself, transparent communication, and active listening- "can transform the way you lead analytical minds. I'll share stories from the trenches, including how showing vulnerability helped me navigate a tough interpersonal moment, and how listening (without jumping in to "fix") sparked a fresh idea from a quiet team member. We'll also talk about the challenges of leading introverted, detail-oriented personnel who thrive on clarity but don't always speak up |
sas programmer, clinical data | source | 5 |
| 44 | Automated Delta Detection: A Scalable R-Shiny Framework for Comparing Clinical Datasets | Prabhakara Rao Burma, Latha Donapati |
In the modern clinical trial landscape, data is characterized by its high velocity and increasing complexity. As studies move toward decentralized models and real-time Electronic Data Capture (EDC) integration, the ability to rapidly identify "DELTA" discrepancies between successive data transfers has become a mission- critical task for Data Monitoring Committees (DMC) and Statistical Programming teams. Traditional methodologies, primarily anchored in static SAS PROC COMPARE outputs, often present a signifi cant bottleneck; these text-heavy listings are difficult for non-programming stakeholders to interpret and lack the interactivity required for high-volume reconciliation. This paper presents a scalable R-Shiny framework designed to modernize the delta detection process. The application utilizes a format-agnostic extraction layer to ingest clinical datasets (SAS7BDAT, CSV, and XLSX) and implements a robust "Delta Engine" powered by dplyr and tidyr |
audit trail, database lock, clinical programming, excel | source | 7 |
| 45 | Automated Quality Checks for SDTM and ADaM Datasets Using R Shiny | Shih-Che (Danny) Hsu, Wei Qian |
In clinical trial analysis programming, SDTM and ADaM data sets are an essential component in the process of ensuring accuracy and reliability of clinical trial data reporting; managing a project that involves numerous SDTM and ADaM datasets along with their corresponding log files can be complex and resource-intensive. Identifying data quality issues-such as inconsistent timestamps, missing values, or log errors-often requires manual inspection across multiple files, which is time- consuming and prone to oversights. To address this challenge, we developed an R Shiny application that automates overall dataset quality checks and presents the results in a centralized, interactive dashboard. Users interact with the application by specifying a folder path containing SDTM and ADaM datasets along with associated log files | adam dataset, data cleaning, clinical trial, excel, clinical data, cdisc | source | 4 |
| 46 | Automating Bioresearch Monitoring (BIMO) Listings Using R | Weishan Song, Wanting Jin, Weiyu Zhou, Margaret Huang |
The U.S. Food and Drug Administration (FDA) requires a BIMO package for pivotal studies to support clinical site inspections. A key component is the subject-level data line listings by site, which provide site- specific safety and efficacy subject data. These listings are often produced by manual programming , requiring programmers to program and validate each individual output from SDTM/ADaM datasets. In large submission packages containing multiple pivotal studies and hundreds of BIMO listings, this approach becomes not only labor-intensive but also introduces the risk of variability in presentation and potential misalignment with the clinical study report (CSR). While the same data elements have already been validated and presented in CSR listings, this duplication of effort represents a significant operational burden that increases with submission complexity | quality control, ods rtf, data cleaning, cdisc | source | 15 |
| 47 | Automating Character Variable Length Updates Using SAS Macros | Carleigh Jo Crabtree | Presentation | character variable, length statement | source | 7 |
| 48 | Automating Git Workflows in SAS with Git Functions | Lleyton Seymour | Whether you are an independent developer or part of a larger organization, Git has become a foundational component of the modern development workflow, and SAS users are no exception. SAS users will typically interact with a Git client in one of two ways: through a command-line interface or through a point-and-click GUI. While both approaches are sufficient, when working with scheduled jobs, flows, or automated pipelines, neither is ideal. This becomes especially apparent in regulated settings where objects, such as models, may be produced by scheduled or automated workflows without a traceable history. Enter Git Functions; these functions enable developers to execute Git operations directly within their SAS code, eliminating the need for manual intervention | data step, sas viya, ods html, proc step, proc sql | source | 10 |
| 49 | Automating Table Generation in Real-World Data Programming: An AI-Assisted Approach | Sachin Heerah, Darren Jeng |
Real-world data (RWD) programming in pharmaceutical research involves generating standardized statistical tables for outcomes research (OR). The Pfizer RWD programming team developed a framework of R functions for creating consistently formatted tables. While this framework standardizes workflows, programmers still manually construct complex function calls for each table. This paper describes RWD OR Output Chat, a Shiny web application that uses large language models (LLMs) and retrieval-augmented generation (RAG) to generate R code from natural language specifications. Generated code is organized into editable code boxes and validated through independent LLM chat sessions that check each function call against its source definition. An in-app table preview allows visual verification. By accepting natural language input, the application also lowers the barrier to entry for programmers building R proficiency alongside SAS | data cleaning, excel, data wrangling, proc tabulate, sample | source | 12 |
| 50 | Be a Multi-Media Wizard: Make Your Output Dance and Sing | LeRoy Bessler | Static and simple presentation of information always adequately communicates, but SAS does offer capabilities to take advantage of more communication channels and methods. Among possibles are audio, video, animation, a marquee for your web page (the analogue for that traveling text at the bottom of a television news program-where it is called a chyron, not a marquee), things that you can do with images (different ways you can put them on a web page), examples of 3D graphics that actually communicate versus the bad examples that ALWAYS distort the information, and even the odd phenomenon of making a line of text twinkle | bar chart, proc step, output delivery system, vertical bar chart, if-then-else, macro loop, data visualization, proc template, time series analysis, macro definition, ods rtf, %if-%then-%else, proc sgplot | source | 49 |
| 51 | Beyond Imitation: Selecting Synthetic Data with Purpose and Precision | Sundaresh Sankaran, Pritesh Desai, Sherrine Eid |
Synthetic data has applications for various areas of life sciences such as clinical trials, data sharing, real - world data, and data imbalances. While synthesization approaches have been variously studied as machine learning, simulation or anonymization problems, they tend to rest on assumptions involving original data distribution, which may not be sufficient or completely known. Through an illustrative example involving SAS Data Maker and SAS/OR (Operations Research) on SAS Viya, we demonstrate a method which views synthetic data as a "selection" problem. In this method, several synthetic observations serve as contenders for selection in a desired dataset and are "selected" based on specified target objectives and rules defining constraints. This method involves augmenting conventional machine learning-based generation algorithms with optimization techniques that solve user-specified objective functions under constraints |
macro variable, sample, clinical trial, sas viya, odds ratio | source | 9 |
| 52 | Beyond Today-s Evidence: How AI-Enabled RWE Will Transform Development Speed, Study Quality, and Real-World Impact | Sherrine Eid | Real-world evidence (RWE) has evolved from a supplementary data source to a foundational pillar of modern drug development, regulatory decision-making, and value demonstration. Global regulators- including the FDA, the EMA, and Japan's PMDA-now formally recognize RWE as a critical component of integrated evidence packages supporting approvals, label expansions, safety evaluations, and post - market commitments. This paper examines how AI-enabled analytics are reshaping evidence generation across the product lifecycle, grounded in a synthesis of 24 primary sources spanning regulatory guidance, clinical trial cost economics, data quality research, NLP/AI benchmarking, commercial platform evaluation, and SAS platform capabilities | clinical trial, audit trail, clinical data, data cleaning, adverse events, sas viya, extract, data governance, clinical research, edc, regulatory submission, data science, normalization, sample, life sciences | source | 15 |
| 53 | Beyond WHODrug: Insight into Concomitant Medication Data Analysis | Song Liu, Martin Nielsen, Rambabu Sura |
Concomitant medication (con-med) is not just about WHODrug B3/C3 coding. ICH guidance requires sponsors to collect con-med. It is important to assess its impact on participant safety, drug-related adverse events, confounding, or additive effects on efficacy endpoints. Con-med can have a study-defining impact on estimands. For example, systemic anti-cancer therapy is considered an intercurrent event under the ICH E9(R1) 4 addendum. It affects the treatment effect on disease progression. In cardiovascular trials, both baseline and on-treatment diuretic dose increase can affect treatment effects in heart failure participants | concomitant medications, intercurrent event, treatment effect, adverse events, proc freq, medical history, data science | source | 9 |
| 54 | Biostatistical Foundations 201: Privacy Preserving Patient Linkage Across Real World Data Sources | Anbu Damodaran | Real-world data (RWD) integration increasingly depends on linking electronic health records (EHRs), claims, and registries to build longitudinal patient trajectories1,2. Absent universal identifiers and under strict privacy constraints, linkage must balance privacy and accuracy. This paper consolidates deterministic and probabilistic linkage3,4, privacy-preserving record linkage (PPRL) methods such as tokenization and Bloom filters5,6. and machine learning7 approaches into a reproducible blueprint geared to statistical programmers. We detail match-quality estimation, precision-recall tuning, propagation of linkage error into downstream analyses, and post-linkage convergence diagnostics (overlap, coverage, temporal alignment, concordance). Operational guidance covers data standardization, blocking, scoring, thresholding with clerical review, continuous QA monitoring, and governance - including salts/keys, auditability, and bias assessment | record linkage, statistical programmer, sample, create table, proc sql, proc freq | source | 8 |
| 55 | Boolean Rhapsody: 50 shades of true Is this the real code? Is this just fantasy? | Charu Shankar | Boolean logic appears simple - conditions evaluate to TRUE or FALSE. In SAS, however, Boolean expressions are numeric in nature, introducing subtle but critical nuances around missing values, function return values, and evaluation context. These nuances often lead to unexpected results, incorrect filtering, and difficult-to-debug logic. This paper presents a practical, pattern-driven exploration of Boolean logic across the DATA STEP, WHERE processing, PROC SQL, PRX functions, and hash objects. Through focused examples and real-world scenarios, it demonstrates how SAS evaluates conditions and how programmers can write clearer, more predictable code. Rather than treating Boolean logic as a binary concept, this paper reframes it as a numeric evaluation model-providing a mental framework and set of patterns that can be applied immediately in practice. Takeaway: sharper Boolean instincts, fewer surprises, and more reliable SAS code |
missing value, proc sql, by group processing, where statement, hash table, automatic variable, end= option, sysrc | source | 36 |
| 56 | Boston Breakthroughs: A Dashboard-Driven Approach to Metadata and Audit Trails with SAS Clinical Acceleration | Frances Gillespie, Laura Watson |
Clinical trial organizations face increasing demands for transparency, traceability, and regulatory compliance. This presentation introduces the concept of growing regulatory expectations in clinical trials, followed by an overview of the importance of metadata and audit trails in ensuring data integrity and submission readiness. An interactive dashboard built using SAS Visual Analytics on SAS Viya will be demonstrated, leveraging metadata and audit trail data from a clinical trial stored in SAS Clinical Acceleration Repository on SAS Viya. The dashboard visualizes metadata changes and audit trail logs, illustrating why understanding and interpreting these elements is critical for compliance and efficient workflows. The work highlights how dashboard driven insights enable clinical programmers, data managers, and regulatory professionals to conduct faster data reviews, improve traceability, and support regulatory submissions through enhanced transparency | audit trail, clinical trial, data integrity, sas viya | source | 9 |
| 57 | Breaking the Shell: Validated R Workflows To Meet FDA Standards | Danielle Stephenson, Audrey Chin, Madeleine Penniston |
For years, open - source programming in clinical research has hovered on the edge of regulatory workflow: admired for its flexibility, yet questioned for its reliability. Atorus set out to challenge that perception by building the tables and listings specifi ed in the FDA's 'Standard Safety Tables and Figures: Integrated Guide" entirely in R using rigorously validated open - source tools. By tapping into the {pharmaverse} ecosystem, our team developed a complete set of reproducible template table and listing pro grams that align with FDA expectations. In addition to adopting community - driven standards, we also created a set of internal R - based templates to support consistency and efficiency across projects. With these internal templates, we expanded beyond the sco pe of the FDA templates by constructing additional template programs for figures and efficacy outputs. This paper outlines our approach, validation strategy, and lessons learned from this process | data cleaning, clinical programming | source | 5 |
| 58 | Bridging the Gap A Python-Word Integration for Detecting Ghost Page Breaks in SAS-Generated RTFs | Jun Yang, Robert T. Stemplinger |
Generating Tables, Figures, and Listings (TFLs) via SAS ODS RTF is the industry standard for clinical trial reporting. However, SAS frequently introduces "extra" page breaks or "orphaned" headers due to margin constraints and font rendering discrepancies. Also, customizable solution integrated business logic such as abnormal indentation in the first row to identify page breaks fit various requirements in different scenarios. Manually reviewing hundreds of pages to ensure no empty pages or split tables exist is time-consuming and prone to human error. This paper introduces a novel Python-based application designed to automate the detection of pagination anomalies. While standard text-extraction libraries often fail to capture the visual "flow" of a document, this tool leverages Microsoft Word's rendering engine as a diagnostic bridge |
quality control, data integrity | source | 8 |
| 59 | Bridging the Gap: Table-Driven SAS Programming as a Pathway to AI in Clinical Trials Statistical Programming | James Sun, Vijaya Nadella, Daniel Qi |
While general-purpose Large Language Models (LLMs) exhibit impressive "out-of-the-box" proficiency in clinical trial workflows and industry data standards , they can already generate SDTM datasets and functional SAS code with reasonable accuracy. However, the "last mile" of meaningful AI integration requires moving beyond general knowledge to accommodate organization-specific programming conventions and highly specialized, proprietary business rules. This paper identifies table-driven programming as the essential bridge to overcome these limitations. By externalizing hard-coded logic into structured, declarative metadata tables, this methodology enhances code maintainability and flexibility while providing the high-quality, structured inputs required for effective LLM training and deployment | sas programming, proc step, data cleaning, proc sql, data validation rules, call execute, user-defined format, sdtmig, data science, clinical trial, macro definition, sdtm domain, clinical programming, trial elements, macro loop, macro variable, dataset, %if-%then-%else, adverse events, cdisc | source | 15 |
| 60 | Building a Model Context Protocol Server for AI-Driven Workflow Automation | Samiul Haque | This paper introduces sastool-mcp, a lightweight Model Context Protocol (MCP) server that enables clinical programmers and statisticians to interact with | clinical programming, data cleaning | source | 4 |
| 61 | Building ADaM Datasets from Scratch using R: A SAS programmer-s Perspective | Patel Mukesh, Nilesh Patel |
In clinical trials, SAS has traditionally been the primary tool for statistical programming, analysis, and reporting. However, the industry is increasingly embracing a language -agnostic approach, encouraging statistical programmers to develop proficiency in multiple programming languages. This shift is driven by the growing adoption of open-source tools, with R emerging as a powerful alternative due to its flexibility, extensive package ecosystem, and cost-effectiveness. This paper documents a SAS programmer's transition to building ADaM datasets in R. In this paper, we will share our experience as seasoned SAS programmers developing Analysis Data Model datasets entirely from scratch using R for the first time. We will provide a detailed, step -by-step walkthrough of variable derivation in R, highlighting the similarities and differences compared to traditional SAS programming methods | adam dataset, sas programmer, proc step, by group processing, change from baseline, missing value, clinical trial, macro definition, statistical programmer, if-then-else, macro variable, cdisc | source | 15 |
| 62 | Building Better Data Science Workflows: Best Practices with Git, GitHub, and Data Version Control (DVC) for Effective Collaboration | Ryan Paul Lafler | This paper presents a practical framework for building reliable and collaborative data science workflows using Git, GitHub, a nd Data Version Control (DVC). It begins by explaining the importance of version control in data science and introduces Git as t he foundation for tracking code changes. GitHub is then presented as a collaboration layer for shared repositories and team -based development. DVC is introduced as an extension to version control that enables efficient tracking and versioning of datasets alongside code. The paper presents practical examples demonstrating structured branching strategies, meaningful commit practices, managing work -in-progress (WIP) safely, and reducing merge conflicts in team environments. DVC workflows are demonstrated to show how dataset changes can be tracked, compared, and restored without storing large files directly in Git repositories | machine learning, data science, cloud storage, clinical trial, sample | source | 17 |
| 63 | Building for the Long Haul: Managing Scope, Refactoring, and CI/CD in Internal R Packages | Huijun An, Blazej Neradilek, Chenchen Yu, Shannon Grant |
How do you successfully carry an internal R package with a small team in a non-profit organization when the needs and team change? We share benefits and challenges in developing and using an internal R package for figures in immunological reports in clinical trials. With our plotting functions used in over 300 reports, we chronicle a transition from a single statistician developer to a developer team over 10+ years. We discuss lessons in scope creep and process improvements, including documentation, bug tra cking and management, traceability, code re-factoring and continuous integration and continuous delivery (CI/CD). We illustrate our re-factoring effort to extend functionality and collaborate more easily. We also share our CI/CD approach, including unit tests for plots (vdiffr package). Overall, the lessons highlight the importance of good software engineering and developmental practices for evolving internal R packages | box plot, clinical trial | source | 9 |
| 64 | Case Study: Integrating ADPPK CDISC Standards into Pharmacometric Programming and Analysis Workflows | Praseeda Rajan, Renuka Hegde, Erin Dombrowsky, Neelima Thanneer, Yue Zhao |
With the release of the CDISC ADaM Population Pharmacokinetic (popPK) Implementation Guide (IG) and anticipated FDA requirements, pharmacometric programmers must adapt to new standards for creating ADPPK datasets. This case study provides a comprehensive guide for integrating ADPPK CDISC standards into pharmacometric programming and analysis workflows. Achieving compliance necessitated extensive workflow changes, from dataset preparation to submission, including accurate metadata alignment and updating internal tools. A key challenge was harmonizing and pooling multiple studies into a single CDISC-compliant ADPPK dataset, requiring mapping of historical variables and inclusion of standardized variables (e.g., PARAM/PARAMCD, AVAL/AVALC, RACE, SEX). Analysis- specific variables (e.g., ARACE, AGEGR1) were derived as recommended, and model files were updated for compatibility with new variable requirements | imputation, data cleaning, population pk, cdisc | source | 6 |
| 65 | Challenges for small-mid size organization to build a GxP Compliant R Environment (CRE) | Peng Zhang, Tai Xie, Peilin Zhou, Christine Matakovich |
Small- to mid-size organizations are increasingly adopting open-source technologies such as R for clinical trial analysis due to their flexibility and cost efficiency. However, building a GxP-compliant R computing environment (CRE) presents significant challenges, particularly in areas such as package governance, validation, reproducibility, and resource constraints. These challenges are further amplified when integrating emerging technologies such as large language models (LLMs), which introduce additional concerns regarding statistical validity, traceability, and controlled execution. This paper discusses key challenges faced by small- to mid-size organizations in establishing a compliant and scalable CRE, including management of external and internal R packages, implementation of validation frameworks, and ensuring statistical consistency across programming environments | data cleaning, clinical trial | source | 7 |
| 66 | Chatting with Your Data, Wherever It Lives: Unlock Insights through Duck DB and Open File Formats | Sundaresh Sankaran, Mary Dolegowski |
Enterprises have shifted their approach to accessing data for analysis and insights. They require rapid insights preferably with minimal data movement. There's greater adoption of open formats like Parquet that reduce data footprint and enable efficient r etrieval, and increased inclination to use a multitude of formats like JSON, CSV and Parquet. As part of recent technology trends, we witness the emergence of Duck DB, an open -source, lightweight query processing engine optimised for analytical workloads. Duck DB should be viewed as a multipurpose query processing engine rather than a traditional enterprise database and can analyse a range of data formats residing in several source areas, making use of native readers without having to copy data. Analytical platforms and packages such as SAS, R and Python offer Duck DB drivers and related capabilities |
proc sql, data dictionary, dataset, reference data, raw data | source | 15 |
| 67 | Closing the Expectation Gap: A Leadership Framework for Clinical Programming Success | Yuka Tanaka-Chambers | Clinical programming (also described as statistical programming) is often perceived as structured work that is predictable, repeatable, and easily scalable. This perception creates a fundamental expectation gap between programmers and non-programmers, where complex analytical work is reduced to a "push- button" process. At the same time, a second gap exists between sponsor organizations and Contract Research Organizations (CROs), driven by differences in operating models, access to data, and regulatory constraints. These dual gaps lead to persistent challenges across clinical studies, including unstable timelines, repeated rework, quality risk, and increasing burnout among programming teams. Sponsors may expect rapid turnaround based on internal experiences, while CROs must operate within structured, audit-ready delivery frameworks | clinical programming, adam dataset, data cleaning, cdisc | source | 14 |
| 68 | Closing the Loop: An Interactive R Shiny Dashboard for EDC Data Visualization and Real-Time Review Tracking | Jun Yang, Yuying Jin |
Clinical data review often involves a fragmented workflow where medical monitors and data managers switch between EDC systems, static PDF listings, and EXCEL trackers. This creates silos between the data and the review process, making it difficult to track query status in real-time. There is a critical need for a centralized platform that combines data visualization with a formal "review and comment" loop. This paper introduces a "Full-Stack" R Shiny application designed to bridge the gap between raw EDC data and structured data review. The application utilizes the R Shiny dashboard framework to provide a multi-tab interface for loading raw datasets (CSV/SAS7BDAT). Interactive visualizations, including patient profiles and safety trend plots, are generated using Plotly and ggplot2. The core innovation is the integration of the DT (DataTables) package with a MySQL database backend |
mysql, excel, clinical data, proc sql, data review | source | 10 |
| 69 | Closing the Loop: Validating AI-Generated SDTM Mappings using CDISC CORE and Synthetic Data | Pietro Belligoli, Constantin Weberpals, Yarhy Flores Lopez |
Generative AI approaches to SDTM mapping often focus on prediction accuracy but lack tight integration with conformance validation, leaving specification and logic errors undetected until late in the data collection or analysis phases. We describe a closed-loop framework that combines generative | data cleaning, sdtm programming, controlled terminology, sdtm domain, data validation rules, vital signs, clinical data, clinical trial, cdisc | source | 7 |
| 70 | Code Hard and Put away Wet: Replacing Hardcoded SAS Software Quality Checks with Data-Driven Design and Defensive Programming Techniques That Validate Code and Control Data | Troy Martin Hughes | You wouldn't ride that pony hard and put her away wet, so why subject your SAS software to the same ill treatment?! Defensive programming describes a risk management strategy that aims to identify threats to software functionality and/or performance before they occur and, where possible, to identify pathways to programmatically mitigate those risks (or to communicate realized threats to sta keholders). This talk introduces defensive programming techniques that can be implemented when software executes, such as verifying SAS program file state (i.e., availability, accessibility) and program file metadata (e.g., FILENAME, checksum, creation date, version). Data-driven software design further espouses the reality that "software" comprises not only code but also the underlying control data that drive that code's functionality and flexibility |
exception handling, proc fcmp, sas base, data validation rules, macro variable, proc step, data cleaning, macro definition | source | 8 |
| 71 | Composite TLFs - A Combined Approach to Data Visualization | Jesse M. Pratt, Rayce Wiggins |
Clinical trial reviewers are routinely required to interpret results across separate tables, listings, and figures (TLFs). While each output serves a specific purpose, reviewing them independently introduces inefficiencies, increases cognitive burden, and raises the risk of missed signals. This paper introduces Composite TLFs, a unified reporting approach that programmatically integrates tables, listings, and figures into single, cohesive outputs. Composite TLFs consolidate logically related summary, subject-level, and graphical information into one output, allowing reviewers to assess results holistically without navigating between files. Examples are presented using simulated data and implemented in both SAS and R, demonstrating that this approach is software-agnostic and readily adoptable across clinical programming environments | bar chart, macro variable, proc step | source | 16 |
| 72 | Consolidation of CDISC ADaM | Cindy Stroupe-Davis, Trevor Mankus, Tatiana Sotingco, Alyssa M. Wittle |
Presentation | source | 4 | |
| 73 | Construction and Evaluation of External Controls Using Propensity Score Methods | Bala Niharika Pillalamarri, Yayu Li, Hongbing Jin |
In randomized studies, subjects are assigned to treated or control groups, ensuring balanced covariates and unbiased estimation of treatment effects. However, in rare diseases and early oncology development, randomized controlled trials are often infeasible due to ethical and logistical constraints. As a result, external control (EC) arms constructed from historical registry, or prior trial data are increasingly used. An EC arm requires alignment of baseline clinical characteristics with treated group to reduce bias, commonly through propensity score (PS) methods. This paper describes the construction of an EC arm and evaluates multiple PS-based adjustment strategies, including trimming, matching, Average Treatment Effect (ATE), and Average Treatment Effect on the Treated (ATT). Propensity scores were estimated using PROC PSMATCH based on key baseline covariates, with covariate balance assessed using graphical diagnostics |
propensity score, sample, treatment effect, average treatment effect, sample size, propensity score matching, proc step, randomization, potential outcomes | source | 14 |
| 74 | Contrast Effects in Curated Observational Data | Shankar Srinivasan, Helen Guo, Yvonne Buttkewitz |
We provide details on weighted analyses to help remove baseline biases in contexts where there are multiple groups and where there is interest in comparisons involving multiple treatment groups, such as pairwise-comparisons or comparisons of one group or a combination of groups to another group or combination. For instance, in a data set with outcome on 5 therapies with 2 in one drug class and 3 in another drug class, there might be interest in comparison of a therapy of interest to other therapies in the drug class or to one or all the members of the other drug class, or in a comparison of the two drug classes. These call for the use of a composite linear string of treatment effects called a contrast. For such composite inferences, we describe modifications to weighting typically used in the two -group setting, which lead to good estimates of composite effects, though conventional analyses using covariates continue to be optimal in the setting investigated | sample, propensity score, proc calis, treatment effect, proc phreg, standard deviation, regression analysis, inverse probability weighting, anova, sample size, logistic distribution, binomial distribution, sample weight | source | 16 |
| 75 | Corticosteroids in severe COVID-19 across molecular endotypes and vaccination status: an Emulated Target Trial approach to benchmark to and extend upon findings from RECOVERY | Xi Jiang, Scott McClain, Ella Pavlechko, Varunraj Valsaraj |
COVID-19 and associated acute lung injury is molecularly heterogeneous. We hypothesized that typical single type treatments and responses may also vary by vaccination status and patient-specific factors. We emulated a target trial to evaluate corticosteroid treatment effectiveness in severe respiratory failure, exploring heterogeneity of treatment effects across vaccination status and clinically derived molecular endotypes. Using a retrospective cohort of 5,000 hospitalized patients at the University of North Carolina Hospital (January 2020- December 2022), we applied an emulated target trial framework to mechanically ventilated patients. Our analysis successfully reconciled the corticosteroids benefits observed in the Randomised Evaluation of COVID-19 Therapy (RECOVERY) trial with real-world data. Notably, the stratification by clinically-linked molecular endotypes revealed distinct populations of responders and non- responders | treatment effect, treatment arm | source | 10 |
| 76 | Creating Reproducible Clinical Output with SASSY - Reporter Package | Vicky Yuan | As the R programming language continues to gain traction in the pharmaceutical and clinical research industries, many SAS programmers are seeking robust and user -friendly reporting solutions that align with established clinical and company standards. Whil e numerous open -source reporting packages are available in R, identifying one that offers both flexibility and familiarity can be challenging for those transitioning from SAS. The SASSY -Reporter package stands out as an exceptional choice, particularly for SAS users, due to its intuitive design and functional similarities to the widely used PROC REPORT procedure in SAS. This paper highlights the key features of the SASSY -Reporter package, emphasizing its ease of use, adaptability to clinical reporting requirements, and the convenience it offers to programmers accustomed to SAS |
preferred term, proc freq | source | 16 |
| 77 | CTCAE v6.0: The Good, the Bad, and the Ugly | Elizabeth Dennis, Grace Fawcett |
The National Cancer Institute's Common Terminology Criteria for Adverse Events (CTCAE) is an important tool for reporting the severity of adverse events as grades. In many clinical trials, these grades are applied to lab results, and they are often programmatically determined in the production of the ADaM lab results dataset. In 2025, version 6.0 of CTCAE was released. As compared to version 5.0, it contains additions, deletions, and revisions. The release included an EXCEL version, with a tab that shows a comparison to version 5.0, and the type of change. In January 2026, an errata was issued with a few corrections. These criteria have evolved over time, with many of the criteria becoming clearer. Unfortunately, some ambiguities remain, which leave questions on how the grades should be programmatically determined. This paper will walk through some of the grades that have been revised and now have clear guidelines, and others where the interpretation is murky |
adverse events, shift table, change from baseline, ctcae grade, clinical trial, cdisc | source | 6 |
| 78 | Current Review of Open Source in New Drug Applications: R & Python | Philip Bowsher | I started a list of submissions that use open source in 2023. I maintain the list at: a. https://github.com/philbowsher/Open-Source-in-New-Drug-Applications-NDAs-FDA The 2026 analysis documents the increased prevalence of R within New Drug Applications and its integration into the submission process, while Python appears with lower frequency. The current landscape indicates that open-source software is utilized as a primary tool for both Sponsor analytic submissions and FDA independent reviewer assessments | data science, regression analysis | source | 3 |
| 79 | Data Without Borders: CDISC Data Hub for Multi-Language Clinical Analytics & AI | Mayank Singh | In the evolving landscape of clinical research, fragmented data environments hinder rapid insights, cross- study analysis, and regulatory compliance. This paper introduces a scalable and flexible approach for centralized clinical data repositories, designed to be agnostic to underlying relational database systems- our implementation leverages Amazon (AWS) Redshift. The framework employs structured SDTM schemas and a dynamic approach for ADaM. Automated, language- agnostic ETL (Extraction,Transformation, Loading) pipelines facilitate seamless data access across SAS, R, Python, and SQL, supporting advanced analytics, machine learning, and meta- analyses. By transforming traditional study - specific storage into an integrated ecosystem, this framework addresses data inconsistency and silos, promotes collaboration among multidisciplinary teams, and ensures compliance with industry standards | machine learning, proc sql, macro variable, if-then-else, clinical data, relational database, proc step, macro definition, cdisc | source | 11 |
| 80 | dbLoadTable: A Robust and Efficient Solution for Bulk Data Transfer in Real-World Evidence Analytics | Li Liu | The rwdtools R package streamlines Real-World Data (RWD) analysis by offering functions tailored to the scale and complexity of Real-World Evidence (RWE) database studies. This presentation highlights one key function: dbLoadTable, which efficiently uploads large local data tables to remote databases. In RWE workflows, analysts often create intermediate datasets locally that contain partial patient or study information. To enrich these datasets with additional variables, such as demographics or outcomes, from other database tables, the local data must first be placed into the same database source. This is the only way to join tables within the same database. Benchmark comparisons show that dbLoadTable dramatically outperforms existing functions such as dplyr::copy_to, DBI::dbWriteTable, and BOPM::rwdex_save (BOPM is a Merck internal R package) in both speed and reliability. For example, when processing a 7.6 MB dataset, dbLoadTable is approximately 300 times faster than alternatives | proc sql, insert | source | 5 |
| 81 | DefinePageChecker: A Python Tool for Verifying Page Number Hyperlinks in Define.xml | Xianhua Zeng | The accuracy of page number hyperlinks in define.xml files is crucial for regulatory submissions in clinical trials. These files often include page number hyperlinks to ensure accurate cross -referencing in the clinical trial report. Discrepancies in these hyperlinks may lead to incorrect or broken links, compromising the integrity of the report. This paper introduces DefinePageChecker, a Python tool designed to automate the verification of page number hyperlinks in Define.xml files. The tool checks whether the hyperlinks correspond to the correct page numbers and missing annotation pages, ensuring that the report's navigation structure remains intact and reliable. The app, source code, and test files are available on my GitHub: https://github.com/XianhuaZeng/PharmaSUG/tree/master/2026/DefinePageChecker | clinical trial, regulatory submission, cdisc | source | 4 |
| 82 | Delegation as a Quality Strategy: Building Accountable Biometrics Teams | Steven Tan | As clinical trials grow in complexity, biometrics functions, including data management, biostatistics, and statistical programming, face unprecedented demands for quality, speed, and scalability. Delegation, particularly through models such as the Functional Service Provider (FSP), has become a strategic mechanism to enhance data integrity, reduce cycle times, and ensure regulatory-aligned execution. Drawing on six real-world case studies and supported by external industry research, this paper examines how structured delegation improves operational quality, governance, and outcomes within biometrics teams | data governance, clinical programming, database lock, clinical research | source | 5 |
| 83 | Designing a modular and interactive visualization tool for DMC | Chen Li, Hong Wang, Shu Chen, Xuan Jiang |
During Data Monitoring Committee (DMC) meetings, it is important to cross -check both aggregated data and individual records from areas such as adverse events, laboratory results, baseline conditions, concomitant medications, and other domains to better understand safety concerns. Traditional static reports make this process time-consuming, often requiring extensive manual searches or additional listings. To address this, we developed a modular R Shiny application that integrates summary tables, listings, figures, and patient profiles into a unified interactive environment. Modules are interconnected through participant identifiers, enabling seamless drill down from aggregate results to subject level detail. The modular structure accommodates both internal and open-source R packages, while allowing expansion with customized features. Key development challenges include coordinating between multiple packages, establishing cross-module communication, and debugging nested functions | concomitant medications, adverse events, proc freq | source | 2 |
| 84 | Detecting Abnormal Page Breaks Using Grayscale Pixel-Density Analysis in R Shiny | Yi Guo | Unexpected page breaks in tables, listings, and figures (TLFs) are difficult to prevent because page capacity is hard to estimate in advance. For example, overlong or newly added footnotes and constrained column widths may lead to truncation, resulting in more rows or larger figures than can fit on a single page. Since each TLF has a unique layout, programmers must still visually review outputs case by case even after dataset validation. While page break issues are relatively easy to detect in shorter outputs, manual review becomes increasingly time- consuming and less reliable as outputs span many pages, making such issues more likely to be overlooked. Inspired by color science, this paper presents a standalone R Shiny tool that applies grayscale pixel-density analysis to support detection of abnormal page breaks, assessing page layout through content distribution rather than visual appearance | standard deviation, error message | source | 28 |
| 85 | Do One Task, Get Another Done for Free: Use DEFX Tags in Comments to Fill Out Your define.xml | Brendan Bartley, Megan Hinger |
Have you ever wanted to take care of two tasks while only doing one? If so, this is the commenting system for you. We all know we need to comment our programs, but sometimes are lax about this. I suspect this may happen less often if the comment would save time and effort filling out the define.xml at the end of a project. At our workplace, code review is also a big part of our validation process, so our comments must ensure that the intent of the code matches what is in the code. The DEFX tag system will save time and effort finding programs and writing comments for the define.xml while encouraging the good coding practice of commenting one's code | proc step, proc export, proc import, excel, proc sql, macro loop, imputation, define-xml, if-then-else, macro variable, retain, proc sort, %if-%then-%else, cdisc | source | 21 |
| 86 | DOSEON: Fuzzy Matching DOSE Date Intervals ON Analysis Dates Across SAS (PROC SQL, SAS Macro, DATA Step, and PROC FCMP) | Inka Leprince, Troy Martin Hughes |
In clinical trial analysis data sets, the treatment variable DOSEON, the treatment dose at record start, was introduced in ADaM OCCDS v1.0 for use in non-ADSL ADaM data sets. DOSEON is typically derived from exposure data by determining if and when an analysis (start) date falls within a treatment dose level start-end interval. Although the definition appears simple, performing the merge of exposure data with another analysis data set is complex because the CDISC data structures do not provide a natural primary key for joining these data sets. Instead, the derivation of DOSEON requires matching a single date to a date range. This paper explores procedural and functional approaches to solving this fuzzy matching lookup operation . Procedural approaches require one or more DATA steps and/or SAS procedures, as demonstrated with PROC SQL joins, a SAS macro, and a hash object lookup within a DATA STEP |
hash table, output delivery system, proc sql, proc step, lookup, adverse events, hash iterator, macro definition, proc fcmp, set statement, composite key, dose, order by, proc compare, do loop, clinical trial, proc sort, date variable, key, data merging, treatment period, hash add, missing value, cdisc | source | 39 |
| 87 | DoxySAS: An End-to-End AI-Powered SAS Documentation Pipeline | Saikrishnareddy Yengannagari | What if your SAS macro library could document itself and answer programmer questions? DoxySAS makes this possible by combining AI-powered documentation generation with interactive chat capabilities, revolutionizing how pharmaceutical organizations manage macro documentation. DoxySAS is a web-based tool that analyzes SAS macro code using Azure OpenAI to automatically generate comprehensive Doxygen-compatible headers including descriptions, parameter documentation, usage examples, and called macro references. The tool intelligently distinguishes positional from keyword parameters, processes bulk uploads simultaneously, and produces consistent, professional documentation in seconds rather than hours. The complete pipeline transforms raw macros into searchable HTML documentation deployed via RStudio Connect, with AI chat injection enabling programmers to interactively query the macro library | macro definition, call symput | source | 5 |
| 88 | Dynamic Patient Profile Plot Development with SAS Graph Template Language | Raghava Pamulapati, Guowei Wu, Danfeng Fu |
Patient profile reporting typically involves collecting all relevant information for a given patient and presenting it as a listing, which is often informative and straightforward. However, patient profile plots take this a step further by visually displaying a patient's data across multiple domains. These plots are not only more visually appealing but also provide a more powerful representation compared to listings. We propose here a solution that involves developing a SAS macro to generate patient profile plots that aligns data from multiple domains on a common study -duration axis, enabling efficient review and identification of potential safety signals for a given patient. The profile consolidates treatment exposure, key laboratory parameters with reference ranges, AEs with timing and severity, and optional concomitant medications, providing a clear visual context for evaluating potential safety signals such as drug- induced liver injury (DILI) | adverse events, concomitant medications, spaghetti plot, graph template language, proc sgpanel, proc step, macro loop, adam dataset, macro definition, %if-%then-%else, record count, cdisc | source | 13 |
| 89 | Dynamic table generation for ongoing studies with unstable or changing cohorts | Haoran Li, Junruo Xia, Yixuan Zhang |
Summary tables are essential for presenting key statistics in clinical trial reporting, yet they often require frequent updates in ongoing studies due to staggered enrollment, cohort expansion, or evolving dose levels. Traditional programming approaches require frequent manual code modifications for each dry run, increasing workload and maintenance risk. To address this challenge, we propose a SAS macro -driven solution that automates the generation of dynamic summary tables. This macro framework dynamically constructs treatment group column names and definitions for PROC REPORT based on the latest ADSL data. The macro automatically extracts treatment group information, calculates Big N values, supports multiple commonly used column header display formats, and allocates column widths proportionally based on a predefined total width |
macro variable, proc step, proc report, clinical trial, baseline characteristics | source | 6 |
| 90 | Early Restricted Unblinded PK Data Access (ERUPA): Framework to Accelerate PopPK and ERES Deliverables Through Data-Centric, Firewalled Workflows | Dheeraj Rupani, Srinivas Bachina, Kiran Kumar Kode |
Population PK (PopPK) and exposure-response (ERES) deliverables are frequently on the regulatory critical path, and programming bottlenecks around data access, specifications, and traceability can jeopardize accelerated submission timelines. ERUPA (Early Restricted Unblinded PK Data Access) and ESAP (Earlier SDTM/ADaM Access for PK and ERES) are AstraZeneca frameworks designed to front-load pharmacometric data preparation by providing restricted early access to unblinded PK and key baseline covariates and by prioritizing post-lock delivery of PK/ERES SDTM/ADaM datasets. This paper describes ERUPA and ESAP specifically from a pharmacometric -programming viewpoint. We outline how CPQP programmers operate inside a GxP firewalled environment; how harmonized PK data standards, PMx DaRT (Data Request Tool), and a standardized R/NONMEM ecosystem (MeRGE) reduce ambiguity and rework; and how timeline templates translate into day-by-day programming tasks | data standard, adam dataset, cdisc | source | 7 |
| 91 | Early Unblinding to Pharmacometrics (PMx) Data: Challenges, Practices, and Benefits | Shweta Vadhavkar, Jing Su |
Pharmacometrics (PMx) analyses play a critical role in Model-Informed Drug Development (MIDD), supporting dose selection, trial design, benefit-risk assessment, and regulatory decision-making. However, PMx analyses depend on pharmacokinetic (PK) concentration data, exposure, and baseline covariates of interest. Of these, PK data can act as a surrogate for treatment assignment and hence can imply the treatment assignment, resulting in restricted access until formal study unblinding. This paper describes the scientific rationale, business need, benefits, and risks associated with controlled early access to PMx data prior to unblinding. Drawing on industry practices shared through the ISOP PMx Data Programming Special Interest Group (SIG), we summarize governance models, firewalled team structures, and standard operating procedures (SOPs) used to mitigate risks of unintentional unblinding and bias | database lock, population pk, data cleaning, clinical research, phase ii, phase iii, regulatory submission, adam dataset, cdisc | source | 6 |
| 92 | Early-Careers Essentials: Practical Checklists for Manual TLF Review | Ingrid Shu, Xinhui Zhang |
In clinical reporting, Tables, Listings, and Figures (TLFs) must be accurate in wording, counts, and formatting in order to support meaningful, high-quality deliverables. New programmers may be tempted to rely on error-free logs and the mere existence of output as evidence of readiness, only to discover that superficial checks miss critical defects, leading to rework and extra review for stakeholders. This paper presents a practical checklist for finalizing TLFs, helping elevate outputs from "ran successfully" to "ready for delivery." Additionally, these checklists help new programmers develop intuition for the critical items to inspect and correct in TLFs. The checklists for TLF review are built around three critical elements: (1) labeling consistency (2) numeric coherence, and (3) aesthetic factors | analysis set, safety population | source | 7 |
| 93 | Easy Code Generation in R: The "macro" package | David J. Bosak | You can write a program. Or you can write a program that writes a program. That is called "code generation". Code generation is advantageous in some scenarios. One advantage is that you can parameterize the outer program, and still produce concise code in the generated program. Another advantage is that the code can be generated in such a way that it is complete, transparent, and readable: as if a human programmer wrote it. This paper will explain how to generate such code in R using the "macro" package. The "macro" package provides a meta-language inspired by SAS Macro, and motivated specifically for the need to generate code easily and efficiently. This package is perfect for creating standard analysis, safety tables, and reusable modules of all kinds. The paper will give an overview of the system and show some simple examples to illustrate how it works | symbol table, macro facility, macro variable, macro function, macro definition | source | 8 |
| 94 | Effective Collaboration Between Business, Technical and Quality Units in Building GCP Systems | Ravi Tejeshwar Reddy Gaddameedi, Tarek Ahammad, Elizabeth Arnold |
In pharmaceutical product development, effective stewardship of regulated analytics environments (e.g., SAS /R tools, clinical data platforms, metadata repositories, and submission pipelines) benefits from a cross-functional governance model that unites Technical (programming/engineering, biostatistics, data management, etc.), Business (clinical operations, portfolio, finance), and Quality (Good Clinical Practice(GCP), Computer System Validation (CSV),Quality Assurance (QA)) leaders under a single accountable framework |
data cleaning, data integrity, inspection readiness, audit trail, clinical data | source | 6 |
| 95 | Efficiency in Action: Automating Bookmarking for CRFs and Other Regulatory Submission Documents | Srivathsa Ravikiran, Sri Raghunadh Kakani, Yang Xu |
Bookmarking Case Report Forms (CRFs) for regulatory submissions is a critical yet often labor-intensive task for statistical programmers. Manual bookmarking in Adobe Acrobat Pro is time-consuming, repetitive, and prone to human error, especially for large, multi-level documents. This paper presents a clear, step-by-step workflow for automating CRF bookmarking using the TRS toolbox (formerly ISI toolbox) plug-in for Adobe Acrobat Pro. By leveraging the toolbox's import/export functionality and EXCEL templates, programmers can efficiently generate, edit, and apply complex bookmark structures with minimal manual intervention. This approach accelerates the submission preparation while improving accuracy, reproducibility, and compliance with regulatory requirements. The workflow is illustrated with practical procedures, template examples, and guidance for integrating screenshots, making it accessible for programmers seeking to streamline their CRF bookmarking |
excel, regulatory submission, statistical programmer, data cleaning, quality control | source | 11 |
| 96 | Efficiency Techniques in SAS 9.4 | Stephen B. Sloan | Using space and time efficiently has always been important to organizations and to programmers in general and to SAS programmers in particular. We want to be able to use our available space without having to obtain new servers or other hardware resources, and without having to delete variables or observations to make the SAS data sets fit into the available space. We also want our jobs to run more quickly both to reduce waiting times and also to ensure that scheduled job streams finish on time and that successor jobs are not unnecessarily delayed. Internal mainframe billing algorithms have always rewarded efficiency. As we move toward cloud computing efficiency will become even more important because the billing algorithms in cloud environments charge for every byte and every CPU second, putting an additional financial premium on efficiency | proc step, character variable, numeric variable, dataset, proc means, proc sort, by group processing, missing value, proc contents, proc freq, sas log, length statement, proc sql, rename statement | source | 9 |
| 97 | Eliminating QC Programming Duplication Through Claude AI-Assisted Independent Code Generation: A Practical Framework for Regulatory-Compliant Validation | Jaime Yan, Jason Zhang |
Quality Control (QC) programming in clinical trials requires independently recreating production programs, creating substantial duplication. This paper presents a framework that uses AI agents-large language model (LLM)-based code generation tools such as Anthropic's Claude-deployed on company-managed infrastructure to generate QC programs (written in Python) directly from specifications. The framework employs operationally separated AI instances with three interconnected mechanisms, all developed by the authors for this study: aQC T race T reethat makes the agent's reasoning transparent and auditable before code generation; aDecision Routerthat systematically selects QC approaches based on dataset class and complexity; and domain-specificAgent Skillsthat encode best-practice patterns for each ADaM class. An internally developed automatedCode Reviewengine enforces programming SOPs across all generated code | independent qc, clinical programming | source | 13 |
| 98 | Embracing Novel Approaches to Automated Causal Inference Framework | Laura Watson, Sherrine Eid |
Our industry perhaps has the deepest understanding of the statement, correlation does not imply causation. Causal claims are directly actionable for policy implementation in a way that traditional association-based claims might not be. This leaves us with a rather glaring and consequential question: how can we infer causation? As the evolution of technology and methods grows, we find ourselves at an exciting time for innovation and potential to exponentially impact causality in our industry. This paper outlines an applied framework for performing causal analysis on observational data in SAS Viya, leveraging automation with the trusted logic in SAS and novel open-source packages. Researchers can now robustly analyze causal inference in real world data, as well as visually analyze the insights generated by the best-in-class analytics on an end-to-end platform | causal inference, data science, proc step, average treatment effect, potential outcomes, treatment effect, propensity score, instrumental variables, robust estimator, sas viya, regression analysis | source | 15 |
| 99 | Engineering secure and reproducible R based clinical programming systems using open source DevSecOps practices. | Indraneel Chakraborty | R is increasingly being adopted in clinical programming, evolving from isolated scripts into reusable functions, packages, and automated pipelines that generate analysis -ready datasets and TLF outputs. As reuse expands across studies and teams, risks emerge, including accidental exposure of credentials, result drift across machines, dependency changes that alter outputs or introduce vulnerabilities, automation failures in non -interactive environments, and unintended leakage of sensitive data through logs or artifacts. This paper presents an engineering roadmap for moving from scripts to packages to pipelines while making security, auditability, and reproducibility inherent qualities of the system | clinical programming, data cleaning, audit trail | source | 11 |
| 100 | Enhancing ADaM PK Datasets to Automate PK TFLs Generation | Jianli Ping, Karthik Sankepelli |
Automated tools for programming output have been increasingly applied to generate safety tables, figures, and listings, which demonstrates improved programming efficiency. Implementing this programming methodology for PK data poses specific challenges, primarily due to differences in how treatment effects are defined when generating PK TFLs. Specifically, the key input variables for PK output automation differs from the conventional treatment variables used in most other ADaM datasets. This discrepancy arises because PK analysis requires treatment effects to be further stratified based on reference dosing days, food effect and other study-specifics. Currently, these PK specific treatment variables are typically derived and managed at the TFL programming level. In this paper, we propose PK group variables (TRTAGxN) and their descriptions that can fit the needs for PK TFLs programming by adding them into ADPC and ADPP | macro parameter, pk parameter, macro call, macro variable, macro definition, cdisc | source | 12 |
| 101 | Enhancing ADaM Specification Validation and Generation of SAS Codes Using LLM through Amazon Bedrock: A Practical Framework | Pavan Kumar Tatikonda, Ryo Nakaya, Sravan Kongara |
Accurate and well-documented CDISC ADaM specifications and efficient SAS programming are critical for producing high-quality clinical trial analysis datasets. However, the manual review and translation of specifications into SAS code is often time-consuming, inconsistent, and prone to human error particularly when specifications evolve over time or vary in structure across studies. Recent advances in Generative Artificial Intelligence (GenAI) present an opportunity to augment traditional statistical programming workflows with intelligent validation and automation. This paper presents a practical, enterprise-ready framework that leverages large language models (LLMs), specifically Anthropic Claude models accessed via Amazon Bedrock, to automate the validation of ADaM specifications and generate corresponding SAS code | data cleaning, excel, adam dataset, phase ii, clinical programming, clinical trial, phase i, cdisc | source | 10 |
| 102 | Enhancing CDISC Standards Implementation (SDTM and ADaM) with PROC FCMP, PROC IML and Macro Loop Integration in Oncology Clinical Trials. | Ajay Gupta, Misikir Tesfaye |
Oncology clinical trials require standardized SDTM and ADaM datasets for regulatory submission, but the complexity of tumor measurements and response evaluation leads to time sensitive and error -prone manual processes. This poster aims to demonstrate the application of PROC IML ,PROC FCMP and a macro loop integration to efficiently create SDTM(TU, AE) and ADaM (ADRS,ADAE) datasets, ensuring compliance with CDISC (Clinical Data Interchange Standards Consortium) standards (SDTMIG v3.4 ,ADMIG v1.2).PROC IML was used for matrix operations to transform tumor data per RECIST 1.1 criteria, creating SDTM TU datasets and deriving Best Overall Response (BOR) for ADaM datasets |
proc step, macro loop, proc iml, proc fcmp, adam dataset, clinical trial, cpu time, regulatory submission, adverse events, cdisc | source | 7 |
| 103 | Enhancing Quality and Efficiency in Clinical Programming with a Python-Based Automated File Comparison Tool | Ratheesh Gunda | Ensuring consistency and accuracy of files across directories and sub-directories is a critical step in statistical programming workflows to ensure that the latest datasets are used for analysis. Discrepancies in file presence or versioning can lead to outdated analyses and quality issues. This paper introduces a Python-based automated file comparison tool with a graphical user interface (GUI) designed to streamline the validation process between hierarchical folder structures and flat directories. The tool performs three core functions: 1. Verifies file presence or absence between folders, 2. Detects TIMESTAMP changes to identify updated or modified files, and 3. Generates a detailed EXCEL report summarizing differences for easy review and documentation. With its intuitive GUI, users can select source and target folders, initiate comparisons, and instantly review results, eliminating the need for manual checks or repetitive library setups |
data cleaning, character variable, excel, clinical programming, raw data | source | 10 |
| 104 | Enhancing Your SAS Viya Workflows with Python: Integrating Python's Open-Source Libraries with SAS using PROC PYTHON | Ryan Paul Lafler, Miguel Bravo Martinez del Valle |
Data scientists, statistical programmers, machine learning engineers, and researchers are increasingly leveraging a growing number of open-source tools, libraries, and programming languages that can enhance and seamlessly integrate with their existing data workflows. One of these integrations, built into SAS Viya, is its pre-configured Python runtime integration, PROC PYTHON, that gives SAS programmers access to Python's open -source data science libraries for wrangling and modeling structured and unstructured data alongside the validated procedures provided in SAS. This paper demonstrates how to install and import external Python libraries into their SAS Viya sessions; generate Python scripts containing methods that can import, process, visualize, and analyze data; and execute those Python methods and scripts using SAS Viya's PYTHON procedure |
proc python, dataset, proc step, sas viya, machine learning, macro variable, data wrangling, sas log, proc contents, sas programmer, work library, libname statement, data ingestion, unstructured data, data cleaning, analytics platform, data science, data pipeline, raw data | source | 13 |
| 105 | Evaluation of Azure OpenAI ChatGPT API as Code Assistance Tools for Statistical Programming in SAS, R and Python. | Ajay Gupta, Misikir Tesfaye |
Statistical programming in pharmaceutical research requires high levels of accuracy, responsibility and traceability. Recent advances in Generative Artificial Intelligence(GenAI) have introduced large language models capable of assisting with code generation through application programming interfaces(APIs).This paper presents a structured evaluation of the ChatGPT model accessed via the Azure OpenAI as a code assistance tool for statistical programming in SAS,R and Python. The evaluation was conducted across three execution platforms, using six predefined prompts grouped by task complexity, ranging from dummy data creation to reusage of macro and function generations. The same prompts group were applied consistently across platforms. A total of 18 language-platform execution scenarios were evaluated. All code generated by ChatGPT was executed without manual modification | numeric variable, random number functions, proc means, macro definition, proc step, clinical programming, sas studio, nominal variable, data science, kaplan-meier plot, proc freq | source | 25 |
| 106 | Experience of an R Programmer Incorporating R in a SAS Studio Flow | Shelby Taylor | Integrating open-source tools into enterprise analytics workflows allows you to increase flexibility and reproducibility in your statistical programming. In this paper, you will learn how to incorporate native R code into a low-code pipeline environment by using the R Runner custom step within SAS Studio on SAS Viya. You also will preview how the new R procedure will provide additional options for integrating R code into SAS. The paper walks through examples of how to fit regression models using base R, transform data with the tidyverse set of packages, and produce visualizations with ggplot2. All analyses run within SAS Studio and remain interoperable with SAS data sets |
sas studio, sas viya, histogram, proc step, regression analysis | source | 16 |
| 107 | Finding Macros Called Within A Directory of SAS Programs | Derek P. Morgan, Tatiana Kazakova |
As an organization heavily dependent on standard SAS macros, BMS needed a way to efficiently document the use of those macros in its programs. In the past, this was a time-consuming, manual task requiring significant effort, and was prone to errors. Either the list was compiled after programming was completed, or it was the programmer's job to document macro use in a header for each program. It isn't difficult for one or two programs, but if you have two hundred programs and tight timelines, it becomes onerous. Also, the manual process isn't good at capturing macros called from within other macros. This method is used to find all the macros and nested macros for a given study and produces an RTF table that can be copy-pasted into an ADRG. The methodology described in this paper only uses base SAS, and it only needs read access to files. It does not modify any of the code it inspects. How long it takes is dependent on how much code is to be processed | macro call, proc step, sample, proc append, proc sql, macro loop, macro variable, create table, order by, macro definition | source | 11 |
| 108 | Following Your Footsteps: Maintaining Traceability in ADaM Datasets | Nancy Brucken, Karl Miller |
Presentation | adam dataset, randomization, adverse events, if-then-else, final analysis, sdtm domain, sample, cdisc | source | 59 |
| 109 | Friction to Flow: LLM-Based Automation of Clinical Data Workflows | Pankaj Attri, Matthew J. Becker |
Clinical trial data submissions for FDA review are often hampered by complex, manual processes. We will be demonstrating an LLM -powered solution that automates the clinical data pipeline and, importantly, establishes end -to-end traceability. The solution a utomates the conversion of raw source data into standardized SDTM, generates analysis -ready ADaM datasets, and creates final TLFs for submission, along with the associated code (used to create datasets and analytical outputs). The other important deliverable of the solution is the lineage graph view. By interpreting the Statistical Analysis Plan, the system attempts to create a direct, visual link between SAP requirements and the datasets that fulfill the requirements. Each TLF is linked to its source ADaM datasets, which in turn trace back through SDTM to the original raw data | clinical programming, adam dataset, mock shell, data cleaning, raw data, sdtm domain, data science, cdisc | source | 8 |
| 110 | From Automation to Evidence: Governing LLMs and AI Agents in Real-World Outcomes Research | Sherrine Eid | Large language models (LLMs) and AI agent frameworks are increasingly deployed in patient outcomes research (POR) for tasks spanning automated phenotype extraction, cohort construction, literature synthesis, analytic code generation, and regulatory dossier preparation. Yet the statistical validity, reproducibility, and governance of these systems remain insufficiently characterized for real -world data (RWD) applications intended to support regulatory and health technology assessment decisions. This paper benchmarks four publicly available LLMs - OpenAI GPT-5, Google Gemini 2.5, Anthropic Claude 4, and Meta Llama 3.1 (open-weight) - alongside six retrieval-augmented generation (RAG) agent frameworks across five governance dimensions: statistical reproducibility, data governance and privacy, transparency and auditability, bias and drift management, and fitness -for-purpose in regulated workflows | regulatory submission, audit trail, clinical trial, training set, sas viya, data cleaning, data science | source | 17 |
| 111 | From Chaos to Clarity: A Programmer-s Perspective on Standardizing Population Pharmacokinetic (PopPK) Data for Regulatory Success | Naveen Muppalla, Shibani Harite |
Population Pharmacokinetics (PopPK) modeling typically follows a multi -stage process encompassing base, intermediate, and final models. This paper emphasizes the critical role of statistical programmer in streamlining the PopPK data workflows and preparing data set s and documents for regulatory submissions. Integrating Phase I dose- escalation studies with Phase III pivotal trials require robust data harmonization and adherence to regulatory standards . We explored two strategies for New Drug Application ( NDA) submission. The legacy approach included creation of NONMEM ready PopPK CSV files, define.pdf and a reviewer's guide. The CDISC approach which included creation of adppk.xpt data set leveraging the ADaM PopPK implementation guide (Version 1.0) with machine -readable define.xml metadata |
imputation, concomitant medications, statistical programmer, sample, regulatory submission, missing value, population pk, cdisc | source | 7 |
| 112 | From Chaos to Consistency: Standardizing External Clinical Data with Excel Power Query | Isaac Vazquez, Jose Hernandez Rivero |
Clinical and real-world data integrations increasingly depend on external data sources that fall outside traditional clinical data pipelines. These sources-such as laboratory vendor files, Clinical Endpoint Committee (CEC) data sets, and operational reports-are often delivered in heterogeneous formats including plain text, CSV, and JSON files. Inconsistent structures and formats frequently lead to manual preprocessing, custom scripts, and increased risk of error prior to SAS integration. This paper presents a practical and scalable approach using Microsoft EXCEL Power Query to ingest, transform, and standardize external data from multiple heterogeneous sources into a single, structured EXCEL output designed for direct consumption in SAS . Power Query is used to perform repeatable data ingestion, parsing, data cleansing, variable standardization, and reshaping while maintaining transparency and traceability |
excel, data integration, clinical data | source | 6 |
| 113 | From ChatGPT to Copilot: Evolving AI Support in SAS and Beyond | Jyoti (Jo) Agarwal | Building on insights from PharmaSUG 2025 Paper SI -294, which demonstrated ChatGPT as a transformative assistant for SAS programming workflows, this paper expands the discussion to a broader ecosystem of AI -powered tools, focusing on GitHub Copilot and Microsoft Copilot and their practical applications in statistical programming. As the pharmaceutical industry moves beyond prompt-based experimentation, Copilot tools are redefining how programmers write, debug, and optimize code across SAS, R, Python, and other analytical platforms. This paper presents real-world use cases where Copilot enhances productivity in clinical trial programming, including automating repetitive tasks, generating structured documentation, and enabling cross -language interoperability | clinical data, clinical programming | source | 10 |
| 114 | From Data Flood to Insight: Efficient SDTM Validation for High-Frequency Sources | Seiko Yamazaki | In recent years, the increasing use of high-frequency data such as eDiaries and wearable devices has led to cases where direct conversion into SDTM results in an extreme increase in record counts and very large datasets. Under these circumstances, running CDISC compliance checks using validation tools can require excessive execution time or even fail to complete due to memory limitations or timeouts. Regulatory authorities may encounter similar challenges when receiving and reviewing such datasets, potentially causing delays in the regulatory review process. From this perspective, this presentation will discuss practical strategies that can be implemented at the SDTM design and mapping stage to reduce validation runtime, enabling attendees to manage the validation process smoothly and efficiently while avoiding memory, timeout, or execution errors | data cleaning, proc freq, sdtm domain, clinical trial, cdisc | source | 6 |
| 115 | From Data to Dossier: Lessons from Cross-Company Regulatory Submissions | Nikita Joseph, Gayathri Mahadevan |
Cross-company pooling and submission presents significant challenges requiring agile strategy adaptation, unique problem-solving skills, and predictive insight. Drawing on personal experiences collaborating in cross-company projects this paper will share practical lessons to drive a successful regulatory submission for products co-developed by pharma companies. We will examine complexities around such pooling strategies, and how shifting company-driven priorities impact programming deliverables. We will explore data challenges caused by differing company standards through examples from adverse events, exposure, pharmacokinetics, and immunogenicity data. Particularly, challenges around data integration and alignment, standard adherence, and maintaining analys is consistency will be discussed. We will then shift our focus to programming review of submission documents like pooling strategy, SAP, CSR, and SmPC | sample, adverse events | source | 7 |
| 116 | From Exploratory Data Analysis to Machine Learning: Continuing My Python Journey | Leon Rod Davoody | This paper builds on my previous work using the Summer Olympics 2016 dataset and shows how Python can be used not just for exploring data, but also for machine learning and making predictions. After learning basic data analysis in my earlier paper, I now use more advanced tools like seaborn and matplotlib for visualization, and machine learning models like Random Forest and Decision Trees. The goal was to predict an athlete's sport based on physical features like age, height, and weight. The results showed that height is the most important factor, with weight also playing a big role. I also looked at patterns in medal performance across different countries and sports like basketball, football, and water polo. Overall, this project shows how Python can be used to move from simple data analysis to more advanced predictions, while still being understandable for beginners. All code is included so others can follow along | machine learning, proc hpsplit, proc hpforest, feature importance, data science, correlation | source | 13 |
| 117 | From Learner to Innovator: A Journey in R Empowered by AI to Enhance Narrative Review in Clinical Studies | Shih-Che (Danny) Hsu | In early 2025, I embarked on a six-month journey to learn R programming, beginning with Pfizer's internal training series. These materials provided step-by-step examples and hands-on exercises that built a solid foundation in R. A learning buddy offered guidance and support, making the experience collaborative and enriching. Throughout this journey, I leveraged AI-specifically Microsoft Copilot-as an additional learning companion. Copilot provided contextual assistance, explained complex R concepts, and offered real -time feedback, making the learning process more intuitive and efficient. I then completed a six-week Posit Academy course, which deepened my understanding of functions, loops, and Quarto for reproducible reporting. Exposure to R Shiny examples sparked my interest in interactive applications. Copilot continued to assist by suggesting optimized code structures and helping debug exercises | clinical programming, clinical trial, adverse events | source | 4 |
| 118 | From Manual to Automated": An SAS and R-Based Toolkit for Scalable SDTM Generation | Hardik Sheth, Eldho Alias |
Clinical trials require the submission of SDTM datasets; however, manual programming of SDTM domains is time-consuming and resource intensive. Fully automated solutions often lack flexibility and efficiency when customization is needed to accommodate varying standards or non-standardized data. This paper presents the development of SAS- and R-based toolkits that automate SDTM generation to improve efficiency and reduce manual effort. The toolkits are built on standardized case report forms (CRFs) aligned with CDASH standards, while retaining flexibility to accommodate study-specific requirements. Standard mapping processes are automated, with options for programmers to perform pre - and post- processing as needed. Implementation of the toolkit reduced SDTM development time by over 30%, while decreasing manual programming effort by over 40% of the traditional total hours spent across multiple studies | data cleaning, clinical trial, clinical data, regulatory submission, sdtm domain, controlled terminology, data review, missing value, cdisc | source | 8 |
| 119 | From Molecular Subtypes to Bedside Decisions: An AI Approach for Personalized Critical Care Recommendations | Xi Jiang, Scott McClain |
The COVID-19 pandemic highlighted the limitations of a one-size-fits-all treatment approach for viral illnesses. Personalized treatment strategies are critical, but in ICU settings, decisions must often be made within hours, making real-time personalization challenging. While genetic data can guide therapy in some settings, it is rarely available in the clinical setting. Machine learning (ML) offers an opportunity by leveraging large clinical datasets and real-world expertise to provide individualized, potentially lifesaving treatment recommendations. We developed an AI-powered framework for personalized therapy in three steps. First, we identified two molecular subgroups of severe COVID-19 patients predicted to respond differently to therapies, patterns that likely extend to other critically ill patients with acute lung injury | feature engineering, machine learning, data cleaning, sample, clinical trial, sample size | source | 8 |
| 120 | From Programmer to Influencer: Strategic Leadership for Statistical Programmers in Clinical Development | Xiaohan Zou, Wei Shao, Yi Yan |
The role of statistical programmers is evolving-from technical executors to strategic influencers shaping clinical development. Building on strategies for bridging functional gaps discussed at PharmaSUG 2025- where strengthening collaboration between programming, biostatistics, and regulatory teams proved critical for delivering high-quality, compliant outputs-this paper takes the next step. We explore how programmers can leverage these collaborative foundations to transition from technical contributors to influential leaders, shaping processes, driving innovation, and guiding organizational change in an increasingly complex regulatory environment. We showcase a recent initiative led by statistical programmers to design an end- to-end FDA Bioresearch Monitoring (BIMO) submission process | statistical programmer, clinical research, clinical programming, inspection readiness, data standard | source | 9 |
| 121 | From SAP to CSR: A Metadata-Driven TFL Workflow | Bhavin Busa | Clinical trial reporting workflows are often fragmented across disconnected tools and manual handoffs, resulting in inefficiencies, limited traceability, and challenges in maintaining consistency from analysis planning through Clinical Study Report (CSR) delivery. While standards such as CDISC ADaM and the recently published Analysis Results Standard (ARS) provide a strong foundation, many organizations still struggle to operationalize these standards across the full analysis results lifecycle. This paper presents a metadata-driven framework that connects key stages of the analysis results pipeline: from Statistical Analysis Plan (SAP) specification through TFL design, automated result generation, centralized review, and downstream CSR integration | data cleaning, quality control, data science, clinical trial, cdisc | source | 7 |
| 122 | From SAS Servers to AI Agentic SCEs: Integrating Agentic AI into GxP-Compliant Biometrics Workflows | Kevin Lee | Biometrics teams in clinical trials are currently navigating a significant transition as traditional Statistical Computing Environments (SCE) move from local SAS servers toward the integration of Agentic AI within modern, multi-lingual ecosystems in SAS, R and Python. The paper demonstrates how Agentic AI could be integrated into SCE environment and how SCE could be built and validated in GxP and 21 CRF Part 11 compliance. First, the paper introduces the concepts of Agentic AI in AI agents and Agentic workflow, and its use cases such as SDTM/ADaM/TFL development and validation, clinical artifacts development, and synthetic data creation. Then, the paper provides a detailed look at the implementation and validation of a cloud-based SCE that is fully GxP and 21 CFR Part 11 compliant. It also illustrates the paradigm shift offered by Agentic AI, where autonomous agents use programming languages as tools to plan, reason, and execute statistical tasks within SCE | data cleaning, clinical research, access control, audit trail, clinical programming, cdisc | source | 5 |
| 123 | From Specification to SDTM at Speed: Deploying the SDTM Engine in Production | Lynn Xiuling Zhang, Jacques Lanoue, Ulf Nielsen |
Clinical research faces a persistent clinical data bottleneck: manual, fragmented workflows delay compliant SDTM delivery. This paper introduces the SDTM Engine, a metadata-driven, multilanguage platform (Python, SQL) that unifies ingestion, specification authoring, and generation of audit -ready SDTM outputs. Centralizing mappings and derivations in a machine-understandable specification (MUS), the Engine collapses mapping and programming into a single, natural-language-like authoring step, reducing EXCEL-based instruction overhead and accelerating delivery with traceable, executable specifications. Key capabilities include pre-defined and extendable derivation functions, the Function Standard Library with governed workflows, comprehensive lineage and traceability reporting, and embedded automation for validation and observability |
data cleaning, sdtm domain, sdtm programmer, proc sql, excel, cdisc | source | 11 |
| 124 | From Static Outputs to Living the Data - A Visualization framework transforming Clinical Data into a Continuous Asset | Neharika Sharma | Keeping unmet patient needs at the center of drug development, delays from the pace of the reporting processes directly translate into lost opportunities. Where power is often said to lie in the data, in the drive for speed, it's worth reflecting - are we truly learning from the data to its potential? The reliance on labor- intensive static TLFs- other than the cost associated, slows adaptation and constrains rapid insight generation and decision-making. This presentation introduces introduction to an R-shiny and Java based Data Visualization framework- a dynamic, interactive approach that re-imagines clinical data consumption. By shifting from static deliverables to curated, self-service dashboards, the approach enables teams to efficiently explore data - filtering, drilling-down, and participant-level interrogations | clinical data, clinical trial, data visualization, concomitant medications | source | 13 |
| 125 | From Static to Dynamic: Leveraging R Shiny for Tumor Response Data Review | Reneta Hermiz, Jing Ji, Amrit Pradhan, Martin Sandel |
In oncology studies that follow RECIST guidelines, programmatically derived tumor response is commonly compared to investigator-assessed tumor response to identify and reconcile discrepancies, ensuring adherence to RECIST. However, the review process can be time-consuming due to the volume of data and need for multiple review cycles. This paper presents the development of an R Shiny dashboard whose purpose is to bypass the tedious and error-prone process of manual review. The application will include the following components: Subset View - A set of custom filters such as discrepant response, stratification, or other categories of interest to subset a selectable Patient ID list. Patient ID selection will trigger display of: 1. Patient Level - Displays data that is unchanged across cycles (randomization date, Best Overall Response BOR/Progression Free Survival (PFS) comparisons). Discrepant comparisons will be flagged with color. 2 | adam dataset, data review, cdisc | source | 10 |
| 126 | From Statistical Programmer to Analytical Partner: Navigating the Future of Biostatistics | Shivani Gupta | The field of biostatistics and statistical programming is undergoing rapid transformation driven by advances in artificial intelligence (AI), increased automation, and the adoption of modern analytical tools. These changes are reshaping traditional programming roles and raising concerns among statistical programmers regarding long-term career sustainability, potential job displacement, and evolving skill requirements over the next five to ten years. This paper explores the future role of the statistical programmer within pharmaceutical and clinical research settings, with a particular focus on those working in regulated environments. It reviews current and emerging trends in AI -enabled analytics, auto mation of routine programming tasks, and the growing integration of complementary tools such as R and Python into established SAS/R -based workflows |
statistical programmer, clinical programming, data cleaning, clinical research, artificial intelligence | source | 6 |
| 127 | From Suspicion to Evidence: Automating Character Truncation Risk Audits with a Parameterized SAS Macro and Review-Ready Excel Output | Xinran Hu | Character truncation issues in clinical analysis datasets are often detected late, when resolution is expensive and traceability back to upstream specifications or source representations can be limited. Even when truncation cannot be conclusively proven post hoc, repeated observations of values reaching the declared character length represent a practical signal that a variable may be operating at its storage boundary and warrants targeted review. This paper presents a parameterized SAS macro utility designed as a risk-based audit for character variables. The macro scans one or more datasets, computes variable-level length metrics (defined length, maximum observed value length, and at-limit frequency), and prioritizes variables using configurable thresholds (minimum hit count and/or proportion among non-missing records, tolerance to allow near-limit screening, and top- N selection). To reduce noise, common indicator patterns (e.g., variables ending in "FL") can be excluded | excel, adam dataset, xls, ods excel, macro definition, character variable, sample | source | 7 |
| 128 | From Tables to Tolerances: The Evolving Role of Statistical Programmers in Risk-Based Quality Management (RBQM) | Vihar Patel | Risk-Based Quality Management (RBQM) has become a regulatory and operational expectation in modern clinical trials with the evolution of ICH E6(R2) and E6(R3), together with the Quality by Design (QbD) framework introduced in ICH E8(R1). While RBQM is often viewed as the responsibility of Clinical Operations, Data Management, and Quality Assurance, statistical programmers play an increasingly vital role in translating risk concepts into measurable and actionable insights. They also act as integrators across Biostatistics, Data Management, and external vendors by assessing the feasibility of key metrics such as Key Risk Indicators (KRIs) and Quality Tolerance Limits (QTLs), as well as the suitability and availability of underlying data sources. This paper presents a role-specific competency model describing the skill sets required for statistical programmers to effectively support RBQM activities | clinical trial, statistical programmer, change from baseline, create table, mahalanobis distance, covariance, proc sql | source | 14 |
| 129 | From Word Clouds to Phrase Clouds to Amaze Clouds: A Data-Driven Python Programming Solution To Building Configurable Taxonomies That Standardize, Categorize, and Visualize Phrase Frequency | Troy Martin Hughes | Word clouds visualize the relative frequencies of words in some body of text, such as a website, white paper, blog, or book. They are useful in identifying contextual focus and keywords; however, word clouds- as commonly defined and implemented -suffer numerous limitations. First, multi -word phrases such as "data set" or "Base SAS" are unfortunately segmented into single words-"data," "set," "Base," and "SAS." Second, desired capitalization often cannot be specified, such as visualizing "PROC PRINT" even when its lowercase "PROC PRINT" is observed in text or code. Third, spelling variations (e.g., single and plural nouns, various verb conjugations, abbreviations and acronyms) are not mapped to each other. Similarly, and fourth, comparable words or phrases (e.g., "PROC PRINT" and "PRINT procedure") are not mapped to each other, representing a further lack of entity resolution |
proc freq, proc fcmp, proc step, sas function, entity resolution, macro put, data step, proc print, relative frequency | source | 12 |
| 130 | Global Macro for Master Tracker | Zhuo Chen, Ted Lystig, Xiaofan Cao, Tuan Nguyen |
In a clinical study, it is important to create and maintain the master tracker for SDTM, ADaM, Tables Listings, Figures (TLF) development and validation for each delivery. Normally a clinical study has hundreds or even thousands of TLF outputs, it is very time-consuming if manually creating it for each delivery. This paper describes a global Macro which can automate the master tracker efforts. It also check different issues and flag them in the master tracker, therefore help team to guarantee accuracy, g ain efficiency and avoid human errors | macro variable, data cleaning, clinical trial, cdisc | source | 8 |
| 131 | Goodbye SAS, Hello R: Practical Workflows for CDISC Standards | Madeleine Penniston, Alyssa M. Wittle |
The adoption of open-source tools in the pharmaceutical industry is rapidly transforming how clinical data is managed, analyzed, and delivered for regulatory review. Among these tools, R has become the leading choice, but what does programming in R really mean for today's statistical programmer? This paper presents an end-to-end CDISC workflow demonstrating how statistical programmers can manage the lifecycle of clinical trial data using R as the primary analytical engine. Beginning with SDTM development, the process illustrates techniques using CDISC standards and demonstrates controlled-terminology mapping using packages such as sdtm.oak. Using a consistent programming approach, raw CRF inputs are transformed into standardized datasets across a variety of domain types. The paper then transitions into ADaM dataset creation, highlighting how R can easily accommodate the Subject-Level Analysis (ADSL), Basic Data Structure (BDS), and the Occurrence Data Structure (OCCDS) datasets | clinical data, adam dataset, clinical trial, if-then-else, missing value, controlled terminology, pharmaceutical industry, data cleaning, dataset, adverse events, clinical programming, date variable, statistical programmer, trial elements, proc transpose, data step, proc sort, proc step, cdisc | source | 12 |
| 132 | Handling multiple screenings and multiple enrollments in SDTM: CDISC and FDA Guidance | Laura Williams, Andrea Gardani |
It is becoming increasingly more common for clinical studies to allow multiple screenings and/or multiple enrollments for the same subject. There is no well-established guidance from CDISC nor FDA about how these subjects should be handled in SDTM for regulatory submissions. Currently, there is a proposal from CDISC about handling such cases in a special-purpose domain, Demographics as Collected (DC), that could be added to future versions of SDTM. This aligns with FDA's suggestions in the Technical Conformance Guide; however, FDA does not provide formal direction either. However, there are some tips that can be taken from current guidance and used in practice, considering that sponsors should discuss the approach with their FDA reviewer prior to submission | sdtm domain, clinical trial, cdisc | source | 7 |
| 133 | Harmonizing History: A Framework for Deriving Line of Therapy in Complex Integrated Summaries. | Bhargav Koduru, Kavitha Guddam, Santosh Reddy Lekkala |
The Integrated Summary of Safety (ISS) and the Integrated Summary of Efficacy (ISE) are vital components of submissions to the FDA and EMA. Their importance has now become more important following the FDA's 2026 guidance titled "Use of Bayesian Methodology in Clinical Trials of Drugs and Biologics." This guidance addresses the frameworks that incorporate data from previous studies, Real -World Evidence (RWE), and non-concurrent controls to improve study design and predictive outcomes. A key factor in successfully harmonizing these diverse data sources is the accurate determination of Line of Therapy (LOT). LOT refers to the order/sequence in which treatments are administered as the disease progresses and plays a crucial role as a stratification factor, especially in oncology. Its impact is twofold: in terms of efficacy, LOT helps distinguish the probabilities of success, as subjects in earlier lines of therapy generally show better responses | imputation, clinical trial, medical history, normalization | source | 15 |
| 134 | Has your SAS being 'MEAN' to your data yet? | Ruth Marisol Rivera Barragan, Isaac Vazquez |
Daily work in the pharmaceutical industry requires extensive data handling and report generation, including the creation of standardized data sets like Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM) data sets to support protocol and SAP (Statistical Analysis Plan) requirements. During data preparation, statistical programmers routinely apply formats, perform mathematical calculations, and structure data to ensure meaningful and accurate presentation. At the reporting stage, SAS procedures such as PROC MEANS and PROC UNIVARIATE are commonly used to summarize and analyze data. However, under certain data conditions, these procedures may produce unexpected or inconsistent results. Such behavior can be difficult to detect and may have downstream implications for reporting accuracy and interpretation |
proc step, data cleaning, proc univariate, proc sort, proc compare, data wrangling, proc means, data science, cdisc | source | 8 |
| 135 | Having Your Cake and Eating It Too: Automated Log Analysis Without Losing the SAS EG Log Window | Steven C. Black | For many SAS programmers, the SAS Enterprise Guide (EG) log presents a familiar challenge: while the log is fully visible in the EG interface, it is difficult to programmatically scan for subtle but important conditions such as uninitialized variables, problematic MERGE BY statements, and implicit type conversions. Redirecting the log to an external file enables automated review but removes it from the EG log window, forcing an uncomfortable trade-off. This paper demonstrates that with a little fancy footwork, we can have our cake and eat it too! Leveraging SAS Enterprise Guide's ability to execute custom code before and after program submission, the SAS log can be temporarily redirected to a physical file for automated analysis and then seamlessly restored and replayed into the EG log window. This approach preserves the interactive EG log experience while enabling robust, programmatic log checks | sas log, sas enterprise guide, macro variable, proc step, proc sql | source | 10 |
| 136 | Hero-in-the-Loop: A Super Squad Approach to SDTM Creation & Validation | Julie Ann Hood | Behind every high-quality, CDISC-compliant SDTM dataset lies a team of data-driven heroes, each with their own unique skillsets and battle-tested expertise. In today's clinical trials, creating and validating submission-ready datasets is rarely a solo mission. It's a full-scale, multi-role operation that calls upon the powers of Data Managers, Standards Managers, Clinical Programmers, and Biostatisticians, each bringing something special to the fight for clean, conformant data. Our heroes may hail from different domains, but they share a common trait: a working knowledge of CDISC standards. Whether it's SDTM variables on an aCRF, CDASH-structured EDC systems, or routine exposure to standardized specs, each role is already familiar with the tools of the trade. This foundational knowledge becomes their secret weapon: breaking down long-standing silos and enabling smarter, more collaborative workflows than ever before | sdtm domain, clinical trial, raw data, clinical data, cdisc | source | 7 |
| 137 | How to Train Your Dragon - Embedding AI in Clinical Workflow. Illustrated through Oncology Swimmer Plots | Sri Pavan Vemuri | This paper presents a blueprint for successfully embedding AI into workflow using oncology swimmer plots as a working example. The framework is built on two pillars: structured prompt engineering and validation checkpoints. Prompts guide the AI step-by-step; checkpoints catch errors before they compound. The oncology visualization is the proving ground - the real contribution is a reusable methodology for any workflow where consistency and reproducibility are critical | swimmer plot, data cleaning | source | 7 |
| 138 | Human Beings Still Needed: Manual ADaM Checks that AI Can-t Do | Sandra Minjoe, Mario Widel |
ADaM Conformance Rules v5.0 contains almost 2000 sp readsheet rows of automatable checks. Running that many checks might seem like it would be suffic ient to ensure that data is ADaM-compliant. However, the ADaM Conformance Rules Guide states that "confo rmance rules are machine-readable (i.e., programmable within computer software) and capable of being implemented by ADaM users", and we are told that manual review should also be done. But what constitutes a manual review? This paper and presentation provide many checks that are not fully automatable (they need a human component), though many of them are partially autom atable. These checks are designed to help you find potential ADaM conformance issues that the fully automatable checks cannot. Adding these types of checks to your review will aid you in determining whether your ADaM data is compliant with both ADaM rules and fundamental principles | dataset, character variable, imputation, adam dataset, proc contents, proc freq, cdisc | source | 9 |
| 139 | Implementation of Quality Tolerance Limits in Statistical Programming | Yang Gao | Quality Tolerance Limits (QTLs), as outlined in ICH GCP E6(R2), support clinical trials by proactively identifying systematic issues that may compromise participant safety or the reliability of trial outcomes. Required for all clinical studies, QTLs are fundamental to protecting trial integrity, safeguarding participants, and maintaining the credibility of study endpoints. To meet these regulatory expectations, sponsors must select critical parameters and set well-justified thresholds that align with the study's primary objectives. QTLs function as early warning indicators, enabling study teams to respond promptly and effectively with mitigation strategies when deviations are detected. Implementing QTLs is a collaborative, cross -functional effort in which statistical programmers play an important role throughout the QTL lifecycle -particularly in ongoing monitoring and final reporting | clinical trial, power analysis, clinical programming, proc freq | source | 4 |
| 140 | Implementing AI Agent-Driven Tools to Accelerate Clinical Research Workflows | Nathan Lee | With the rapid emergence of large language models (LLMs), generative AI has evolved from a conversational tool into a powerful platform that has accelerated and changed the ways industries use automation. Beyond just a prompt-level interaction, LLMs now enable the development of Agentic AI tools and workflows to augment statistical programming and increase automation within clinical research. This paper explores the usage of these AI agent-driven tools and the design/workflow of such systems. Specifically, the primary systems this paper focuses on are an SAP (Statistical Analysis Plans) generator, a Clinical Trial Protocol generator, and a TFL (Tables, Figures, and Listings) Validator. This paper first outlines how to design workflows for building such AI tools. Then, this paper explains the internal logic of each application. Next, this paper describes the steps it takes to communicate effectively with both the LLMs and the user | clinical trial protocol, clinical trial, clinical research, data science, data cleaning | source | 13 |
| 141 | Implementing Dynamic Time Warping in SAS 9.4 Using PROC IML: An Alternative Approach for Time-Series Model Evaluation | Yida Bao, Philippe Gaillard, Wei Yao, Zheng Zhang, Rui Wang |
Dynamic Time Warping (DTW) is a useful method for comparing time-series signals when timing shifts or delays are present. Common evaluation metrics such as Mean Squared Error (MSE) focus on point -by- point differences and often fail to reflect similarity in overall shape or timing. As a result, models that visually match the behavior of a biological signal may still receive poor numerical scores. In this paper, we implement DTW in SAS 9.4 using PROC IML and provide a practical way to compute dynamic distance s between time-series signals directly within SAS. Using both simple examples and real ECG data, we show that DTW captures waveform similarity more effectively than traditional error -based measures when temporal misalignment exists. This work provides SAS users with a straightforward and interpretable tool for evaluating time-dependent data |
proc step, proc iml, sample, time series analysis, missing value | source | 13 |
| 142 | Implementing Laboratory Toxicity Grading for CTCAE Version 6 and Beyond | Keith Shusterman, Mario Widel |
CTCAE Version 6.0 has been released, further clarifying toxicity grade terms and definitions from Version 5.0. However, CTCAE is not the only standard for toxicity grading. Other criteria also exist, including the grading criteria from the Health and Human Services Division of AIDS (DAIDS) for adult and pediatric adverse events, as well as the FDA guidance on the toxicity grading scale for healthy adult and adolescent volunteers enrolled in preventive vaccine clinical trials. We will show a flexible method for deriving CTCAE grades that can handle cases where the grading derivation requires information external to the lab value itself, such as the FDA toxicity grading guidance, as well as any existing version of CTCAE, including versions up to 6.0 | ctcae grade, excel, clinical trial, laboratory data, call execute | source | 6 |
| 143 | Improving AI SAS-to-R Code Migration via an Intermediate Design Document Layer | Junze Zhang, Amy Zhang |
The pharmaceutical industry is increasingly migrating clinical programming workflows from SAS to R . SAS and R have resemblance with one another and perform similar functions, but they also differ in some fundamental ways. "Direct" AI translation from SAS to R often reverts to block-by-block literal conversion (Bosak & Varney, 2025; Cheng et al., 2025). This can produce non-idiomatic R code with higher maintenance burden and subtle logic defects, especially when SAS programs rely on sort-dependent BY- group semantics (e.g., first./last., retain, and merge) and SAS-specific missing-value behavior. We propose a design-first, two-step workflow that inserts an intermediate design document layer between SAS and R | imputation, missing value | source | 7 |
| 144 | Improving Risk-Based QA in Outsourced Studies Using Cross-Domain ADaM Derivation Comparisons | Pallavi Sadhab | In outsourced clinical trials, sponsor oversight often relies on a risk-based quality assurance (RBQA) model rather than full independent reprogramming of ADaM datasets. While RBQA effectively verifies derivation accuracy within individual ADaM domains, it does not ensure that conceptually related derivations are implemented across multiple datasets align with each other. This implicit assumption of cross-domain consistency represents a blind spot that can compromise analytical coherence, especially for time-to-event (TTE) endpoints, composite outcomes, and derivations dependent on shared clinical concepts such as treatment exposure, event dates, and censoring logic. This paper presents a structured framework for cross-domain ADaM derivation checking designed to complement RBQA without expanding scope into full reprogramming | inspection readiness, hazard ratio, cdisc | source | 10 |
| 145 | Insights and Experience Sharing with Patient-Reported Outcome Data Analysis in FDA-s Submission | Jingyuan Chen | This paper discusses the implementation of recent FDA guidance on patient-reported outcome (PRO) data in regulatory submissions, including practical challenges, methodological adjustments, and benefits for clinical trials. Drawing on experiences in | clinical trial, data science | source | 8 |
| 146 | Interactive Safety Data Visualization Platform: Transforming Adverse Event Analysis Review Through Dynamic Dashboards in Clinical Trials | Nishanth Chinthala, Alexandr Hromcenco |
Traditional safety data presentations in clinical study reports rely heavily on static tables, requiring manual review of extensive tabulations to identify adverse events (AEs) and serious adverse events (SAEs) of special interest. This process is time-intensive, tedious, and prone to oversight, particularly given the regulatory guidance requiring comprehensive safety endpoint presentations including confidence intervals, Kaplan-Meier estimates, and treatment comparisons at System Organ Class (SOC) and Preferred Term (PT) levels. We developed an R Shiny web application featuring interactive adverse event safety tables with advanced filtering, searching, and collapsible hierarchical display capabilities. The platform was built using pre-analyzed datasets from statistical computing environments without performing in-app derivations, thus avoiding the need for additional validation | adverse events, confidence interval, clinical trial, hazard ratio, data cleaning, system organ class, data visualization, intercurrent event | source | 5 |
| 147 | Introduction to Plotting with the PROCS Package | David J. Bosak | The "procs" R package aims to simulate several SAS procedures in R. For instance, the package has functions like "proc_freq()", "proc_ttest()" and "proc_reg()" that replicate the basic functionality of the corresponding SAS procedures. A major feature missing from these replicas has been the ability to generate plots. That missing piece has been rectified in a recent release of the "procs" package. This paper will discuss which functions now support plots, and how to generate them. The paper will give several examples demonstrating plot options, and also show how they can be exported and used in a report | bar chart, proc step, proc freq, regression analysis, histogram, qq plot | source | 11 |
| 148 | Investigator-Initiated Trials: Navigating Statistical Programming Challenges with Practical Solutions | Vikas Patil, Ramesh Sundaram |
Investigator-initiated trials (IITs) are essential for advancing clinical research, particularly in exploring novel therapeutic approaches and addressing unmet medical needs. Unlike industry -sponsored trials, IITs are often driven by academic researchers and often operate under constrained bu dgets, limited operational infrastructure, and diverse data collection systems. From a statistical programming perspective, these constraints can translate into heightened risk across the end-to-end pipeline ranging from data ingestion and transformation to analysis, reporting, and inspection readiness. This paper outlines key programming challenges in IITs, including data integration, analytical implications, compliance requirements, and operational limitations. Solutions include adopting CDISC standards data transformation, meta-data driven programming frameworks, strategic alignment to navigate the regulatory requirements | competing risks, concomitant medications, regulatory submission, clinical programming, data cleaning, adverse events, clinical trial, data science, cdisc | source | 6 |
| 149 | ISO 8601 and SAS-and R! A Practical Approach | Derek P. Morgan, Marckenley Mercie |
The ISO 8601 standard for dates and times has long been adopted for clinical data by regulatory agencies around the world. While there are many homemade solutions for working in this standard, SAS has many built-in solutions, from formats and informats to the IS8601_CONVERT routine, which painlessly handles durations and intervals. As R gains popularity, several packages are available to handle this mandated standard. This paper will detail both SAS and R capabilities around this standard | datetime, imputation, sysdate9, character variable, sample, clinical data, date variable, time, options statement | source | 17 |
| 150 | It-s a Wonderful Lifecycle: Translating Statistical Programming into Modern Analytics Development | Steve Nicholas | Emerging technologies such as Shiny and open-source analytics frameworks are increasingly being introduced into clinical programming environments that have long relied on established statistical programming processes. While the tools may be new, many of the principles required for successful adoption are already familiar. This paper reframes modern analytics applications through a direct comparison of the software development lifecycle (SDLC) and traditional statistical programming workflows. Using a one-to-one mapping, it translates artifacts such as the Statistical Analysis Plan (SAP), mock shells, ADaM specifications, dry runs, and QC programming into analogous stages of analytics application development. User stories and feature requirements serve a role similar to the SAP, while wireframes function as the equivalent of mock shells, enabling early alignment on purpose, scope, and decision making before development begins | clinical programming, mock shell, independent qc, data science, raw data, data cleaning, cdisc | source | 3 |
| 151 | Keynote Presentation: Between Hype and Reality: Posit's Blueprint for Responsible GenAI in Life Sciences | Tareef Kawaf | Presentation | data science, regulatory submission, data cleaning | source | 28 |
| 152 | Leading with Presence from Afar: Building Trust and Engagement in Distributed Biometrics Teams | Steven Tan | Distributed biometrics teams have become the industry norm, often spanning various geographical locations and time zones. However, within this context, an inherent challenge is effective communication to ensure efficiency and quality of output in cross- function teams across time zones. This paper proposes a practical three-principle framework to address this challenge: (1) predictability; (2) priorities in synchronous collaboration; and (3) technical leverage. The first two pillars focus on reducing coordination friction by identifying issues early and prioritizing partner-facing communication within limited overlap windows. However, the central argument of this paper is that long-term effectiveness in distributed teams depends on the third pillar: leverage created through technical assets | macro parameter, clinical programming | source | 5 |
| 153 | Lessons to Guardrails: Operationalizing Early Checks for FDA Submission Readiness | Jeff Xia | Managing lessons learned from numerous submissions can be challenging and difficult to track consistently. This paper introduces a practical method to transform lessons into guardrails through a phase-based checklist embedded between Last Subject Out (LSO) and Database Lock (DBL), enhancing quality and consistency in studies with filing potential. The checklist was developed by extracting insights from past oncology submissions, staff meetings, monthly Lunch-and-Learns, and periodic Spotlight Projects; converting these insights into actionable statements; and assigning each item an owner, timing, and verifiable actions. The framework addresses common pitfalls, including CSR/TFL alignment, metadata currency, aCRF readiness, investigational drug mapping consistency, disciplined dry runs, unblinded DMC workflows, compliance checks, log hygiene, and submission readiness across eCTD components | data cleaning, adam dataset, cdisc | source | 6 |
| 154 | Leveraging LLMs in Data Science Web Applications: Beyond the Chat Interface | Tyler Rowsell, Nandini Thampi, Dishank Jani, Hao Xu |
Large Language Models (LLMs) have become ubiquitous in modern web applications, yet their implementation is often understood to be limited to conversational chat interfaces. This narrow consideration overlooks the transformative potential of LLMs as backend analytical services that can power data science workflows through strategic prompt engineering and response parsing. This paper explores architectural patterns for embedding LLM capabilities into analytical web applications, where the model operates as a backend service rather than solely a user-facing chatbot. We demonstrate how structured prompt engineering, combining explicit output schemas, delimiter -based serialization, and multi -stage API orchestration, enables LLMs to perform complex subroutines. As representative use cases, we present this approach through two complementary AI capabilities within an R Shiny application | excel, clinical programming, stage 3, stage 2, mock shell, error message, data science, stage 1, clinical data, exception handling, data cleaning | source | 18 |
| 155 | Linear versus Log-Log Confidence Intervals for Kaplan-Meier Survival Estimates: Statistical Rationale and Practical Implementation using SAS 9.4 | Prayag Shah | Kaplan-Meier (KM) methods are routinely used to analyze oncology time-to-event endpoints such as overall survival (OS), progression-free survival (PFS), and duration of response (DoR). Although the Kaplan-Meier point estimate is invariant to the confidence interval (CI) method, the uncertainty around that estimate depends directly on how the interval is constructed. Linear confidence intervals, derived on the raw survival scale using Greenwood's variance, can extend beyond the admissible probability range and can be misleading when survival probabilities are near 0 or 1 or when the number of observed events is small. Log-log confidence intervals instead apply a transformation that stabilizes variance, improves the normal approximation, and yields asymmetric confidence limits that remain within valid probability bounds | confidence interval, sample, survival analysis, progression-free survival, proc step, overall survival, treatment arm | source | 8 |
| 156 | Machine Learning Models for Predicting Diabetes Using the PIMA Indians Dataset | Leon Rod Davoody | This project looks at how machine learning can help predict diabetes early. I used a dataset of 768 people and tested two models: Logistic Regression and Random Forest. Some of the data had errors, so | logistic regression, proc hpforest, regression analysis, logistic distribution, machine learning, confusion matrix, roc curve | source | 11 |
| 157 | Maintaining a Multi-Lingual Code Inventory in Real-World Evidence | Joshna Reddy Nimmala, Yu Feng |
As healthcare claims databases continue to evolve in structure and complexity, statistical programmers are increasingly challenged to maintain consistency, efficiency, and compliance across Real-World Data (RWD) analysis. To address these challenges, programmers rely on multi-lingual code inventories (including SAS, R, and Python scripts) and adapt them as needed to support Real -World Evidence (RWE) generation. However, without a centralized and dynamic approach, these inventories can become fragmented, outdated, and difficult to manage. This paper presents a scalable framework for maintaining a centralized RWE code inventory that supports detection of structural changes in RWD sources. By identifying key structural triggers, such as additions or changes in variable names, coding systems, or data schema, this framework enables intelligent searches across the code inventory to flag impacted scripts and suggest updates | statistical programmer, excel | source | 6 |
| 158 | Managing Unblinded Activities Internally: The Independent Statistical Analysis Team (iSAT) Model | Hong Wang, Shu Chen, Chen Li |
The independent Statistical Analysis Team (iSAT) was established in 2017 as a sponsor -employed, fully unblinded, and independent analysis group to manage unblinded activities internally. Initially created to support Pharmacovigilance for anticipated event reporting, iSAT's role has expanded to include Data Monitoring Committees (DMCs) and Interim Analyses (IAs). The team operates under strict firewalls - physical, electronic, and organizational-to ensure confidentiality and maintain study integrity. Processes include secure data environments, hierarchical separation, and controlled communication. For anticipated event analyses, iSAT receives limited datasets excluding efficacy data, while IA workflows involve blinded teams providing programs that iSAT executes post-unblinding. For DMCs, iSAT functions as both independent statistician and programmer, enabling rapid turnaround times and confidential handling of additional requests | interim analysis, data science, clinical trial | source | 4 |
| 159 | mkheader: An R Package for Automated Generation and Management of Program Headers in Clinical Trial Programming | Gabriela Piasecki, Laura Frederick |
In clinical trial programming, maintaining accurate and up-to-date program headers is essential for regulatory compliance, traceability, and audit readiness. Program headers capture critical metadata such as study name, input datasets, authorship, and revision history. As R gains traction in pharmaceutical programming, the lack of standardized tools for automated header generation within R leads to inefficient, error-prone, manual processes that increase compliance risks. The mkheader R package addresses these challenges by automating the creation and management of program headers tailored specifically for clinical trial programming. It automatically extracts metadata - including study, author details, program date, and R version - and seamlessly integrates user inputs to generate consistent, comprehensive headers | clinical trial, data cleaning, r package | source | 8 |
| 160 | Modern Data Science with SAS Viya Workbench: Unified Development with SAS, Python, and R | Shelby Taylor | SAS Viya Workbench is a modern, code-first analytics environment that brings SAS, R, and Python together within the SAS Viya ecosystem. It supports both learning and professional analytics workflows through flexible interfaces and integrated development tools. Getting started involves launching sessions, selecting compute resources, and navigating a project-based file system. Core functionality includes executing SAS programs, reviewing logs and results, and managing output files, with SAS notebooks available for interactive and exploratory analysis. The environment also enables development in Python and R within the same workspace. Collaboration and reproducibility are supported through integration with GitHub for version control, code review, and traceable workflows, along with connectivity to external data sources such as Snowflake and Google Cloud Storage | sas viya, sas enterprise guide | source | 13 |
| 161 | Modernizing Clinical Research Analytics with Cloud-Optimized SAS Procedures | Jim Box, Mary Dolegowski |
As clinical research organizations continue migrating analytical workloads to the cloud, they gain access to scalable, multi-threaded computing environments capable of accelerating processing across datasets of any size. This session explores a new generation of cloud-based SAS procedures designed to take advantage of this modern architecture. We will examine how these procedures differ from their traditional SAS 9 counterparts, highlight performance and usability improvements, and discuss practical considerations for adopting them in real-world clinical analytics workflows | proc step, sas 9, sas viya, proc mixed | source | 5 |
| 162 | My DIY Swiss Army Knife of SAS Procedures: A Macro Approach of Forging with My Favorite PROCs | Jason J. Su | Here I take advantage of SAS macro facility and forge these following four (4) extremely popular procedures into one (1) Swiss-Army knife (SAK)-styled macro %pfs (the acronyms): PROC PRINT, PROC CONTENTS (not in the acronym), PROC FREQ, and PROC SQL. Controlled by a mode-switch parameter (MODE), the macro can fashion out any one of the 4 procedures in a succinct version supporting popular options in various procedures, such as OBS, FIRSTOBS, WHERE, SHORT, VAR, etc. The new macro has the capacity to carry out my most-frequent jobs from such procedures, such as selectively printing some records from a dataset, displaying its data structure, quickly deriving certain variable frequency, counting certain variables, etc. Based on the spirit, fellow programmers are encouraged to create their own version of the macro %pfs |
proc step, macro conditional, proc print, proc contents, proc sql, proc freq, sample, macro variable | source | 11 |
| 163 | Navigating Career Transitions: From Programmer to Executive Leader | Steven Tan | This paper examines the career transition from individual contributor roles in clinical programming and biostatistics to executive leadership within the biometrics and functional service provider (FSP) industries. Drawing on practitioner experience and established leadership frameworks, two discrete transition phases are identified: from technical execution to team leadership, and from team leadership to organizational influence. The paper articulates the competencies, mindset shifts, and common pitfalls associated with each phase. Actionable guidance is provided for aspiring leaders navigating this trajectory. The central argument is that technical proficiency, while necessary, is insufficient for sustained leadership effectiveness. Success at the executive level demands a fundamentally different orientation toward people, communication, strategy, and organizational culture | data science, clinical programming | source | 7 |
| 164 | Navigating Early Career Challenges in PK/ADA Statistical Programming | Diyu Yang, Sandeep Meesala |
Entering the pharmacokinetic (PK) and anti-drug antibody (ADA) programming field presents unique challenges distinct from traditional clinical programming. New programmers often face difficulties understanding complex clinical data structures, navigating domain-specific standards, and efficiently managing programming workflows. In addition, limited experience with debugging, validation, and data integration can hinder productivity and reduce confidence. This paper summarizes key challenges faced by new PK/ADA programmers and offers practical strategies to overcome them through mentorship, hands-on projects, and the use of available training resources and automation tools | data cleaning, adam dataset, clinical data, controlled terminology, sample, population pk, regulatory submission, sdtm domain, clinical programming, statistical programmer, clinical research, clinical trial, raw data, cdisc | source | 11 |
| 165 | Navigating the Statistical Programming Strategies for Cytokine Release Syndrome (CRS) and ICANS in Oncology Clinical Trials. | Murali Kanakenahalli, Vamsi Kandimalla |
Cytokine Release Syndrome (CRS) and Immune Effector Cell-Associated Neurotoxicity Syndrome (ICANS) are two of the most critical and often dose- limiting toxicities associated with novel immunotherapies, particularly CAR T-cell therapy within oncology clinical trials. Accurate and standardized reporting of these adverse events (AEs) is paramount for assessing the risk- benefit profile of these revolutionary treatments and ensuring patient safety. This paper provides a comprehensive review, from the statistical programming perspective, of the evolution of data collection, standardization, and reporting of CRS and ICANS. We first establish the clinical context and critical importance of these syndromes. We then delve into the challenging pre-MedDRA era, illustrating how programmers employed complex, symptom-based logic to link Adverse Event (AE) data with separate, customized syndrome- tracking Case Report Forms (CRFs) to generate meaningful safety metrics | adverse events, clinical programming | source | 9 |
| 166 | Oncology Solid Tumor Subcutaneous vs Intravenous Late-stage Study Analysis | Sumanjali Mangalarapu, Chuqing Chen, Anilkumar Anksapur |
This paper presents practical programming approaches for analyzing data of a subcutaneous versus intravenously administered study treatment, with pharmacokinetics (PK) designated as the primary endpoint. It focuses on the creation of key Clinical Data Interchange Standards Consortium (CDISC) Analysis Data Model (ADaM) datasets, their mock shells and programming logic used for key summary tables supporting the endpoints. Programming content in this paper highlights primary and secondary endpoints derivations. Because PK as the primary endpoint is uncommon for late-stage solid tumor oncology trials, the programming required tailored analytic strategies, including model-based PK parameter estimation and specific endpoint derivations | sample, adam dataset, mock shell, duration of response, primary endpoint, secondary endpoint, pk parameter, overall survival, randomization, data science, cdisc | source | 11 |
| 167 | One Study, Many Regulators: Submission-Ready Data Package for Multi-Region Filings | Himanshu Patel, Chintan Pandya |
Global clinical trials are routinely submitted to multiple health regulatory authorities, including the FDA (US), EMA (EU), PMDA (Japan), and N MPA (China). While CDISC standards provide a unified technical framework, differences in regional regulatory requirements, such as eCTD structure, data expectations, validation requirements, and review-tool sensitivities, can create significant challenges during submission preparation. This paper presents a practical, programmer -led framework for building a submission-ready data package that supports multi-region filings with minimal region-specific changes. Using oncology studies as an example, the paper discusses strategies for designing region-agnostic datasets, managing region-specific requirements through metadata and documentation, and maintaining robust traceability from raw SDTM ADaM Tables, Listings, and Figures (TLFs) | data cleaning, adam dataset, clinical trial, database lock, regulatory submission, adverse events, clinical data, raw data, cdisc | source | 10 |
| 168 | One Word Can Make All the Difference(s): Strengthening Validation Practices with PROC COMPARE | John M. LaBore, Joshua M. Horstman |
PROC COMPARE is a cornerstone of validation in pharmaceutical processes- especially for confirming independently programmed outputs, verifying transformations, and supporting submission-ready analyses. Programmers often rely on the message: "Note: No unequal values were found. All values compared are exactly equal." Yet users that are unaware of the nuances of PROC COMPARE may make false assumptions based on this statement. In several common clinical-data scenarios, PROC COMPARE's default output can mask discrepancies, particularly when comparing derived variables, handling missing or special values, or validating large analysis datasets. This paper highlights four specific situations where these implicit assumptions may lead to misleading conclusions in double-programming and regulatory validation contexts |
proc compare, proc step, data cleaning, sas log, adam dataset | source | 5 |
| 169 | Operationalizing Generative AI in Regulated Analytics: Applied Implementation Patterns for Enterprise Deployment | Lida Gharibvand | Generative artificial intelligence is rapidly transitioning from experimental use to enterprise deployment across regulated industries such as pharmaceuticals, financial services, and life sciences. These environments require strict standards for auditability, reproducibility, and controlled data access. While modern analytics platforms support validated workflows across Python, R, and cloud-based ecosystems, integrating generative AI capabilities introduces new operational and governance challenges. This paper presents a platform-agnostic framework for integrating generative AI into governed analytics systems. The approach is based on a layered architecture that preserves the authority of validated analytical outputs while enabling natural language interaction, automated narrative generation, and AI-assisted workflows | data cleaning, analytics platform, regulatory reporting, data science | source | 16 |
| 170 | Operationalizing Real World Data for External Control Arms: An End to End Framework for Rare Disease and Oncology Trials | Anbu Damodaran | Randomized controlled trials (RCTs) remain the reference standard for causal inference, yet in rare diseases and precision oncology, RCTs are frequently infeasible or ethically constrained. Real-world data (RWD), from electronic health records (EHRs), registries, claims, and linked assets, can be transformed into regulatory-grade real-world evidence (RWE) to construct external control arms (ECAs) that contextualize single-arm trials and accelerate access to therapies1,2. This paper presents an end-to-end framework to operationalize RWD-based ECAs, spanning target-trial emulation, bias identification and mitigation, data provenance and linkage, and CDISC-aligned SDTM and ADaM programming with reviewer-oriented traceability. We introduce a trust architecture that combines semantic harmonization with clinical-grade QC | causal inference, randomization, cdisc | source | 7 |
| 171 | Optimizing Clinical TFL Review with Python and Power BI: A Reproducible Workflow to Reduce QC Time and Improve Traceability | Vijaya Lakshmi Cherakam, Latha Donapati |
Clinical Programming and Biostatistics teams frequently operate under significant time constraints while being responsible for producing accurate, consistent, and submission -ready Tables, Figures, and Listings (TFLs). Traditional manual TFL review processe s are resource -heavy, and susceptible to oversight, particularly in large or complex clinical studies where multiple data transfers were expected. This paper presents a practical, open-source driven framework leveraging Python and Power BI to enhance and streamline the TFL validation process in clinical trials. The proposed approach integrates automated data and metadata validation using Python, along with independent cross -validation of stati stical summaries through interactive Power BI dashboards. These dashboards enable reviewers to rapidly identify discrepancies, trends, and anomalies across outputs | data cleaning, adverse events, proc means, clinical trial, adam dataset, demographics table, regulatory submission, sas output, excel, independent qc, cdisc | source | 10 |
| 172 | Path to Consistent Clinical Graphics: An R Shiny Gallery | Michelle Harwood, Austin Taylor |
Consistency in clinical reporting is critical, yet figure styles often vary across studies and teams. Limited awareness of modern plotting capabilities often leads teams to revert to outdated or simple visualizations. We developed an R Shiny application that serves as a centralized gallery of commonly used clinical figures. Each figure is paired with interactive customization and replicable R code. Our goal is to standardize figures and coding practices to improve cohesion, reproducibility, and efficiency a cross projects. The application provides a gallery of commonly used figures, such as the Kaplan-Meier, forest, and waterfall plots, while allowing users to tune statistical layers (confidence intervals, censoring marks) and layout options (faceting, annotations) through an intuitive interface | spaghetti plot, waterfall chart, hazard ratio, forest plot, treatment arm, bar chart, box plot | source | 12 |
| 173 | Patient-Level, Dose-Stratified Swimmer Plots for Comparative Adverse Event Time-Course Assessment in Clinical Trials Using SAS PROC TEMPLATE | Qingwei Hu, Zhaoyu Xie, Ji Qi |
Background: Subject-level adverse event (AE) data are critical for clinical safety assessment in new drug applications. However, standard summary tables and listings provide limited visibility into AE time course, including onset, duration, recurrence, and resolution(etc.), and can hinder intuitive comparisons between active treatment and control arms. Methods: We describe a reproducible swimmer-plot methodology implemented with SAS ODS Graphics using PROC TEMPLATE to visualize AE trajectories at the individual participant level while incorporating treatment arm and dose exposure. Each participant is represented by a horizontal timeline spanning their treatment duration, with grouping by placebo and dose-stratified treatment cohorts. AE episodes are displayed as time-stamped intervals, allowing the representation of multiple occurrences per participant |
adverse events, swimmer plot, proc template, proc step, proc freq, clinical research | source | 5 |
| 174 | Perspectives on Leading Effectively in Platform Trials: Leadership and Technical Approaches | Zhen (Laura) Li | Platform trials-run under a master protocol with sub-study-specific protocols-accelerate innovation by adaptively evaluating multiple therapies within a unified, evolving framework, presenting distinctive technical and leadership challenges. This presentation draws on my experience as a product lead programmer for an in-house platform trial and shares how I addressed these challenges while leading a study programming team. On the technical side, establishing and maintaining programs and specifications that balance consistency with adaptability is foundational to high-quality deliverables. Practical examples will show how these approaches flex to diverse analysis requirements and evolving protocols. On the leadership side, effective study management is grounded in comprehensive planning, thoughtful resource allocation, and proactive, solution-focused communication, and supported by fit-for-purpose tools that streamline workflow and collaboration | treatment arm, resource allocation, interim analysis, data migration, clinical programming | source | 7 |
| 175 | Pharmacometric Analysis Dataset Generation Process: Data, Roles & Tools from a programmers Perspective | Srinivas Bachina, Dheeraj Rupani, Hasi Mondal, Kiran Kumar Kode |
Presentation: A GxP-aligned, end-to-end programming workflow enables timely, high-quality, reproducible pharmacometrics datasets that support exploratory work and regulatory pathways. | source | 9 | |
| 176 | Picking Up the Pieces: Implementation of (the forgotten) ADaM Naming Fragments | Richann J. Watson, Karl Miller |
In working with ADaM standards, trying to build data sets and their variables can be a repetitive process that can become tedious as creating meaningful variable names within the 8-character limit. As opinions vary, even our own ideas can depend on the day-to-day, so it's good to be reminded that ADaM fragments exists and are required to help in the consistency of naming conventions and helping with regulatory review efficiency. Earlier versions or "client specifications" veer off path over time, so it is good from time to revisit the more common fragments as well as look at the less common fragments | time variable, imputation, cdisc | source | 7 |
| 177 | Post-DBL Programming Update Tracker: Automating Revision Capture to Strengthen Audit Readiness, Oversight, and Compliance Across Programs and ADaM Specifications | Yunyi Jiang, Christine S Teng |
Regulatory inspections are a critical component of the submission process, serving as a safeguard to confirm the safety, efficacy, and compliance of pharmaceutical products. For management and governance stakeholders, demonstrating compliance, performing pre-inspection checks, and proactively avoiding CAPAs (Corrective and Preventive Actions) are essential. After Database Lock (DBL), even minor updates to code or specifications can create significant audit and compliance risks if documentation is incomplete or inconsistent. Audit readiness depends on maintaining a single, traceable record of what changed, when it changed, and why. This paper presents a SAS-based tool that tracks post-DBL updates to programming and ADaM specifications. It converts technical logs into oversight-ready evidence, including standardized EXCEL outputs, discrepancy alerts for risk -based review, and a non-destructive update mode that preserves user-added commentary while appending new findings |
excel, database lock, inspection readiness, macro parameter, cdisc | source | 8 |
| 178 | Practical Lessons in AI-Assisted Metadata Conversion: From Database Specs to EDC and SDTM - Successes, Pitfalls, and Practical Implementation | Jianlin Li, Andy Shen |
Metadata-driven transformations (CDASH → SDTM → P21) remain a labor-intensive process across clinical programming teams due to differences in EDC implementations, sponsor conventions, and interpretation of the SDTM Implementation Guide (IG). This paper presents a practitioner's perspective on using AI assistance to accelerate metadata processing, workbook generation, and SDTM quality control, while maintaining traceability and regulatory alignment. Rather than focusing on high -level summaries of CDISC standards, this paper emphasizes practical experiences: dealing with Rave SDS inconsistencies, refining mapping rules, applying derivation logic, generating SDTM domain reference workbooks, and integrating AI-generated metadata into existing QC pipelines. The goal is to demonstrate how AI can reduce manual overhead while reinforcing-not replacing-controlled review and validation expected in regulated submissions | sdtm domain, sdtmig, sdtm programming, normalization, data cleaning, cdisc | source | 11 |
| 179 | Programming Challenges in Developing PRO Analysis Datasets Under FDA's New Submission Guidance | Weiwei Guo, Caizhi Huang |
Patient-Reported Outcomes (PRO) data are measurements of a patient's health status, symptoms, functional abilities, health-related quality of life, and treatment satisfaction that are captured directly from the patient, without clinician interpretation. Because PRO collection depends on active patient participation, missing data is common, posing substantial challenges for statistical programming and downstream analysis. In November 2023, the U.S. Food and Drug Administration (FDA) issued "Submitting Patient-Reported Outcome (PRO) Data in Cancer Clinical Trials," which outlines expectations for CDISC SDTM and ADaM datasets and recommends specific tables, listings, and figures (TLFs) for oncology submissions. In September 2025, our study team received an FDA information request to provide PRO TLFs in accordance with this guidance | adam dataset, disposition table, safety population, cdisc | source | 10 |
| 180 | Protocol Analysis, Optimisation and Generation: Artificial Intelligence Enables a Unified View | Sundaresh Sankaran, Sherrine Eid |
A successful clinical trial maximizes multiple objectives defined by relevant primary and secondary endpoints, enrolment and retention, statistically significant results and rigorous evaluation. To design a comprehensive, executable and data-driven protocol, inputs are sourced from clinicia ns, epidemiologists, statisticians, programmers, and compliance officers from a protocol review board to ensure that all requirements are met. These activities involve significant financial and quality costs. Each step of the way is a potential introduction for quantifiable error. Existing solutions tend to be siloed and only address one task at a time. We propose a unified approach to minimise errors and inefficiencies in clinical trials through an end-to-end solution for protocol generation which allows seamless handoffs | clinical trial, sas viya, adverse events | source | 10 |
| 181 | PSMATCH: Propensity Score Matching of Clinical Trial Data with External Control Arms | Ginger Barlow | Pharmaceutical and biotech companies have major financial incentives to lower research and development costs and get products to market faster. Patients have urgent needs for these treatments to diagnose, cure or alleviate symptoms of diseases and conditions. External control arms using Real World Data (RWD), such as registries and platforms, can facilitate research into rare diseases when a randomized controlled trial would be challenging to perform. Using these external control groups provides a more reliable comparison to evaluate safety and efficacy than a single-arm trial would. Prilenia has utilized external control arms for studies in treatment of Huntington's disease (HD) and amyotrophic lateral sclerosis (ALS commonly called Lou Gehrig's disease) | propensity score, proc step, clinical trial, propensity score matching, sample, clinical research, sample size, proc means | source | 8 |
| 182 | Python for Survival Analysis: Kaplan-Meier and Reverse KM Plots Made Easy | Girish Kankipati, Bala Rajesh Jakka |
Survival analysis plays a key role in clinical trials by providing insights into time -to-event outcomes such as overall survival and progression-free survival. Researchers most widely use the Kaplan-Meier (KM) estimator to estimate and visualize survival probabilities over time. This paper presents a practical approach for generating KM plots using Python, a programming language that clinical data analysts increasingly adopt. The paper discusses techniques for plotting KM curves for single and multiple treatment groups, adding confidence intervals, and customizing charts for regulatory reporting. Using Python libraries such as Matplotlib and Plotly, analysts can create both static and interactive visualizations that support reproducible and automated workflows. The paper also highlights the Reverse Kaplan-Meier (RKM) method, which estimates follow-up time by treating censoring as an event | clinical trial, confidence interval, treatment arm, survival analysis, censoring indicator, clinical data, regulatory reporting, kaplan-meier estimator, clinical research, proc means | source | 20 |
| 183 | Quantifying Expression Divergence to Identify Candidate RNA-Binding Proteins Modulating Nonsense-Mediated Decay Across Human Tissues Using t-SNE Embedding Analysis | Liangwei He | Nonsense-mediated mRNA decay (NMD) is a post-transcriptional regulatory mechanism that degrades transcripts containing premature termination codons (PTCs). To prioritize candidate RNA-binding proteins (RBPs) as potential regulators of NMD, we developed a divergence-based framework using GTEx transcriptomic data. Within each tissue, samples were embedded in a t-SNE space and quantified by calculating Euclidean distances between different expression groups. We summarized these results to a global Ratio-Score and compared with a correlation baseline. Both methods capture overlapping, but different NMD-related expression patterns. For example, the Ratio-Score metric prioritized a smaller subset of canonical NMD factors, which performed better for curated NMD-associated regulators. Meanwhile, our framework showed that strong separation can sometimes reflect tissue identity rather than NMD regulation | correlation, sample, proc means | source | 13 |
| 184 | R Package Management in LSAF: Challenges and Solutions | Praneeth Adidela, Sagar Koona |
R is increasingly recognized as a powerful tool for statistical analysis and reporting in clinical research. The SAS Life Science Analytics Framework (LSAF) provides an integrated system for managing, analyzing, reporting, and reviewing clinical research data. With the release of version 5.3, R is now integrated alongside SAS. However, in regulated environments, direct downloads of R packages from CRAN or other external sources are often restricted. This paper presents a workflow for installing and managing R packages in LSAF using internal CRAN -like repositories and local libraries. It outlines key challenges and offers practical solutions for effective R package management within LSAF | clinical research, life sciences, data science | source | 7 |
| 185 | R-Based Translation of Japanese Characters in Clinical Datasets for Regulatory Reporting | Hardik Sheth, Roshan Stanly |
Clinical studies conducted in Japan or multinational trials frequently generate clinical datasets containing unique and regional specific characters across data sources such as electronic data capture (EDC), SDTM, ADaM, and analysis datasets. Although modern systems support Unicode, many legacy SAS- based clinical reporting and submission workflows remain constrained by encoding limitations, restricting reliable processing of non-ASCII text. These constraints introduce risks to data consistency, traceability, and timely delivery of analysis outputs. This paper presents an R-based approach for translating Japanese characters into English to support integration with established SAS- and R-based clinical programming pipelines | lookup, regulatory reporting, clinical trial, cdisc | source | 7 |
| 186 | Ready for Next Level SDTMs and ADaMs Compliance with End-to-End Processing? | Sunil Kumar Gupta, Tomás Sabat Stofsel |
From specifications to results, smarter tools enable better SDTM and ADaM compliance for higher quality submissions. This is achieved by anticipating common issues and checklists during the SDTM and ADaM mapping process. While many organizations have SOPs for QC, smarter organizations incorporate continuous monitoring of program development. First, SDTM and ADaM specifications and SAPs are imported for metadata processing and compliance checking. This metadata is essential for cross-referencing the logic of all derived SDTM and ADaM variables. While SDTM and ADaM structures and rules must pass the litmus test, the single story message from raw data must be transparent and consistent with SDTMs and ADaMs. Finally, SDTM and ADaM required variables must be created and all raw data must be mapped to SDTMs. With this end-to-end system, onboarding new members to the programming team becomes easier because the documentation and all related programs can be quickly explained | data cleaning, raw data, independent qc, clinical trial, missing value, controlled terminology, proc freq, adverse events, cdisc | source | 6 |
| 187 | Real World Data and CDISC - An Evolving Journey | Shuo Cao, Venkat Rajagopal |
Real-world data (RWD) and real-world evidence (RWE) have become essential components in modern clinical research, regulatory decision-making, and healthcare delivery. RWD, originating from electronic health records (EHRs), insurance claims, patient registries, and digital health tools, captures patient experiences in routine clinical practice. Unlike traditional randomized clinical trials (RCTs), which operate under controlled protocols and narrowly defined populations, RWE provides insights into treatment effectiveness, safety, and utilization across heterogeneous populations in real -world settings. The integration of RWD into regulatory and research workflows presents several challenges, including structural heterogeneity, semantic differences, and varying data quality. To address these challenges, interoperability frameworks, controlled terminologies, and standardized data models are critical | cdisc | source | 4 |
| 188 | Regression Analysis Made Easy Using R | Zheyuan Yu, Kirk Paul Lafler, Zichun Gao, Jiaxin Xu, Zeqi Li, Ruochen Shao |
Regression analysis is a foundational technique in data science and statistical modeling, enabling analysts to quantify and interpret relationships between a dependent variable and one or more independent variables. This paper presents a practical introduction to the concepts, common types, and real-world applications of regression analysis using the R statistical programming language. Through clear explanations, simple examples, and reproducible R code, readers learn how to develop, interpret, and evaluate regression models, including simple linear regression, multiple linear regression, and logistic regression. Emphasis is placed on model assumptions, interpretation of results, and practical considerations relevant to applied analytics | regression analysis, proc reg, simple linear regression, multiple regression, logistic regression, outlier, sample, logistic distribution, correlation, box plot, imputation, response variable, stepwise selection, missing value, normal distribution, interval variable, feature importance, qq plot, likelihood function, interquartile range, exploratory data analysis, proc freq, nominal variable, cook's distance, data science, data cleaning, predictor variable, confidence interval, data transformation, proc means, bar chart, binary variable, dataset, model validation, clinical programming, r-squared, histogram, feature selection, residual, life sciences | source | 32 |
| 189 | Reproducing the SAS DATE and TIME formats with {fmtr} package in R | Chen Ling, David J. Bosak |
As pharmaceutical organizations transition from SAS to R for clinical reporting, preserving SAS-compatible date, time, and DATETIME formats remains a critical challenge for regulatory submissions. In current workflows, SAS formats can be applied during XPT export; however, this approach restricts formatting to the transport file and does not allow SAS-consistent formatted values to be generated or reviewed directly within the R environment. As a result, programmers cannot fully inspect, validate, or quality-control formatted outputs in R prior to export. The open-source {fmtr} R package addresses this gap by providing the first complete, in- R replication of the SAS DATEw., TIMEw.d, and DATETIMEw.d display formats. These implementations allow SAS-compatible formatting to be applied natively within R environments, enabling consistent presentation throughout tables, listings, figures, and validation workflows, not solely at the point of XPT creation |
format statement, date variable, elapsed time, datetime20., sysdate9, time. | source | 13 |
| 190 | Reviewing and identifying issues in TFL macro parameter values with R shiny tool | Mydhili Chelikani, Ajay Kumar Tirkey |
The R language supports creation of interactive user interfaces using the Shiny framework. A Shiny U ser Interface is interactive and visually intuitive tool that helps reviewers identify issues quickly and expedites the review process through efficient quality-control checks. This paper illustrates how to leverage TFL (tables, listings, and figures) macro parameter-value datasets to rapidly retrieve relevant information and improve QC workflows. Additionally, the tool identifies the macro programs used to generate TFLs and quantifies the outputs produced by each program, facilitating integration with the program development and validation tracker. Validation programmers can use the tool to determine how many outputs were generated and to identify which ones require validation by cross-checking them against the mockup | macro parameter, data cleaning | source | 5 |
| 191 | Running Python from SAS: A Practical Comparison of Available Approaches | David L. Ward | Python has become one of the most widely used languages in modern data science and analytics. Its large ecosystem of libraries provides capabilities ranging from machine learning and natural language processing to advanced data visualization and web services. Because of this expanding ecosystem, many organizations that rely heavily on SAS are increasingly interested in incorporating Python into their analytical workflows. | proc step, proc fcmp, proc python, sas function, sas programmer, dataset, rest api, machine learning, exception handling, proc http, web service | source | 12 |
| 192 | SAS and R for Expanding Data for Pharmacometrics Analysis (PMx) Analysis Data Sets | Jeffrey Rathbun, Rebecca Humphrey |
Pharmacometrics (PMx) Analysis is performed with a time-ordered data set. The data set contains observation records (Pharmacokinetic (PK) concentrations and/or Pharmacodynamic (PD) endpoint values), dosing records, any demographic, vital sign, laboratory test, or addition al classifying / qualifying data of interest. Typically, this data is contained in SDTM and/or ADaM data domains that are assembled into the PMx Analysis data set. SAS and R are programming languages that are used to accomplish this. This paper focuses on expanding data in PMx Analysis data set builds and how that is handled by SAS and R. Data is often provided on specific dates or over a range of dates instead of daily records. Dosing information, subject data (e.g. weight), and simulated patient data are examples where expanding data is needed. R methods examined will include use of the tidyverse package and the combination of several functions | do loop, sample, adam dataset, output, date variable, retain, proc step, cdisc | source | 8 |
| 193 | SAS for Microsoft 365: Integrating SAS Programs, Data, and Reports Across the Microsoft 365 Ecosystem | Shelby Taylor | SAS for Microsoft 365 bridges the gap between advanced analytics and everyday productivity tools by integrating SAS Viya directly into Microsoft EXCEL, Microsoft Word, Microsoft PowerPoint, and Microsoft Outlook. This paper provides an overview of SAS for Microsoft 365 and demonstrates how analysts can prepare data and generate insights in SAS Viya, then seamlessly explore, update, and share results within familiar Microsoft applications. SAS Visual Analytics reports can be accessed, filtered, and embedded as live objects in EXCEL, enabling users to insert charts and tables that remain linked to the underlying SAS data. The paper also highlights inserting SAS data tables into EXCEL for local exploration, uploading EXCEL-based data back into SAS Viya, and executing SAS programs directly from Microsoft applications with results embedded in documents and spreadsheets |
excel, sas viya, proc freq, xls | source | 23 |
| 194 | SAS Macro Debugging Techniques for Mere Mortals | Kirk Paul Lafler | SAS macro programming is powerful, but when things go wrong, debugging can feel complicated, frustrating, and time-consuming. This Hands-On Training is designed for SAS programmers who understand macro basics but struggle to diagnose and fix macro-related issues efficiently. Using a synthesized Framingham Heart Study dataset (500 observations, 22 variables), participants will learn how to systematically debug macro programs by leveraging SAS system options, diagnostic tools, and structured troubleshooting approaches. The session emphasizes real-world debugging scenarios, including resolving macro variable scope issues, identifying compilation vs. execution errors, handling quoting pitfalls, and interpreting cryptic log messages. Through guided exercises, attendees will gain practical experience using tools such as MPRINT, MLOGIC, SYMBOLGEN, and OPTIONS SOURCE2, as well as techniques for isolating logic errors, validating input parameters, and testing macro-output incrementally | macro variable, macro definition, macro facility, sas log, macro put, proc step, if-then-else, macro quoting function, macro debugging, options statement, regression analysis, data cleaning, macro loop, %if-%then-%else | source | 35 |
| 195 | SAS Packages - an Ask About Anything Game | Bartosz Jabłoński | Modern data-focused languages, like R or Python, have vast ecosystems for building packages (i.e., reusable, structured, and efficient code containers). Those environments allow their users to share their ideas, inventions, and code in an easy, almost seamless way. Why haven't SAS, with its pro- found and historically well-established impact on data analysis, embrace such a marvelous idea? If this or similar questions about SAS packages bothers your sleep, fear no more! This article's purpose is to answer them and give you a solid foundation for future work with SAS Packages. Table of contents | sas packages, macro variable, macro definition | source | 24 |
| 196 | SAS Performance Tuning Techniques (From Slow to Scalable) | Kirk Paul Lafler | In today's data-driven environments, inefficient SAS programs can quickly become bottlenecks, wasting computing resources, delaying insights, and frustrating users. This Hands-On Training workshop demystifies SAS performance tuning by focusing on practical, high-impact techniques that deliver measurable improvements. Using a synthesized Framingham Heart Study dataset (500 observations, 22 variables), participants will learn how to identify performance issues and apply optimization strategies across DATA steps, PROC SQL, and macro processing. The session emphasizes real-world diagnostics using SAS system options (e.g., STIMER, FULLSTIMER, MSGLEVEL, MPRINT) and demonstrates how to reduce I/O, minimize CPU usage, and streamline execution. Attendees will explore indexing strategies, efficient WHERE clause usage, dataset compression, hash objects, and macro-optimization techniques. Each concept is reinforced through guided exercises that compare inefficient vs |
memory usage, proc step, cpu time, performance tuning, elapsed time, options statement, proc sql, where statement, character variable, proc sort, hash table, length statement, sas log, set statement, regression analysis, dataset, by group processing, data wrangling, compress=yes, if-then-else, missing value | source | 18 |
| 197 | SAS Programming techniques for efficiency and code optimization | Jayanth Iyengar | There are multiple ways to measure efficiency in SAS programming; programmers' time, processing or execution time, memory, input/output (I/O) and storage space considerations. As data sets are growing larger in size, efficiency techniques play a larger and larger role in the programmers' toolkit. This need has been compounded further by the need to access and process data stored in the cloud, and due to the pandemic as programmers find themselves working remotely in distributed teams. As a criteria to evaluate code, efficiency has become as important as producing a clean log, or expected output. This paper explores best practices in efficiency from a processing standpoint, as well as others | proc step, proc sort, cpu time, dataset, proc means, proc sql, proc append, by group processing, if-then-else, sas log, where statement, data merging, sample, numeric variable, sample size, sas programmer, lookup, data=, character variable, drop statement, missing value, sas base, index, raw data, options statement, hash table, set statement, class variable, key performance indicator, subsetting if | source | 27 |
| 198 | SAS to R: A Practical Bridge for Programmers | Jyoti (Jo) Agarwal | As the analytics landscape continues to evolve, R has emerged as a versatile and powerful tool for data analysis, visualization, and statistical modeling. While SAS remains a trusted platform in many industries, R offers distinct advantages such as flexibi lity, open -source accessibility, and a rich ecosystem of packages for advanced analytics. For SAS programmers, transitioning to R can present challenges due to differences in syntax, workflow, and programming approach. Building on insights from the 2025 SI -294 paper on SAS programming efficiency, this paper provides a practical and hands-on bridge for SAS users entering the R environment. Part 1 focuses on foundational setup, basic syntax, and most importantly modern data manipulation using R and the tidyverse. It highlights parallels between SAS and R while demonstrating how familiar SAS concepts such as DATA STEP transformations and PROC based summaries can be translated into readable and efficient R workflows |
sas programmer, data wrangling | source | 10 |
| 199 | SAS viya dynamic visualization of data | Jumin Geng | SAS Viya enables powerful, interactive, and scalable data visualization capabilities that enhance analytical decision-making. The dynamic visualization methods allow for more effective display of high dimensional data which can improve the interpretation of various dimensions of clinical trial data. This paper demonstrates several dynamic visualization techniques including crosstab visualizations with in- cell bar charts, butterfly charts, and panel charts. These techniques help users quickly identify patter ns, compare metrics, and communicate insights for clinical trial data effectively | sas viya, clinical trial, clinical data, bar chart, adverse events | source | 5 |
| 200 | Schema-Preserving Generation of Clinical TLF Templates and Executable R Code via Iterative LLM-Guided Debugging | Jaime Yan, Ming Yang |
Manual authoring of Tables, Listings, and Figures (TLFs)-also referred to as mock shells or mock TLF templates in industry practice-for Clinical Study Reports (CSRs) is a resource-intensive bottleneck in reg- ulatory submissions, prone to human error. Conforming to strict ICH E3 and CDISC ADaM conventions is critical, yet current methods lack efficiency and scale. This study investigates the automation of this pro- cess using large language models (LLMs), focusing on two critical questions: (1) which generation methods most effectively improve the quality of regulatory-compliant TLF templates, and (2) can these templates be reliably translated into executable R code? Through a comprehensive analysis of 1,999 instance-matched bootstrap experiments, we evaluated five generation methods across three major LLM providers | data cleaning, regulatory submission, effect size, adverse events, bootstrap, correlation, clinical programming | source | 18 |
| 201 | SDTMIG v4.0: Are You Ready For It? | Kristin Kelly | The imminent release of the CDISC SDTMIG v4.0 / SDTM v3.0 represents a pivotal moment for SDTM standards. As a major version update, it introduces significant changes that sponsors should begin preparing for now. Key updates include support for Multiple Subject Instances (MSI), the transition from SUPPQUAL to Non-Standard Variables (NSVs), and metadata restructuring aligned with SDTM Model v3.0. The -BLFL variable has been removed and Sections 1-4, which provide guidance across SDTM domains, have been reorganized for improved clarity and usability. Variables are now organized into Variable Groups, enhancing structure and interpretation. New domains such as Event Adjudication (EA) and Gastrointestinal Findings (GI) further expand the ability to handle more types of data. Also noteworthy are updates to the Protocol Deviations (DV) domain and specimen-based Findings domains | clinical trial, cdisc controlled terminology, protocol deviations, disease response, cdisc | source | 12 |
| 202 | Smarter, Faster, Better: GenAI-Driven Authoring for Data Reviewer's Guides | Christina Scienski, Christine Rossin |
Leverage generative AI (GenAI) to streamline the creation of Data Reviewer's Guide (DRG) for regulatory submissions (e.g., FDA, PMDA, NMPA) by intelligently re-using existing regulatory documents such as statistical analysis plans (SAP), Protocols, annotated case report forms (aCRFs) , and internal metadata files. Streamline writing process by creating uniform documentation across all therapeutic areas and assets. Establish consistency in content and quality. * Improves authoring efficiency, i.e., resources and time. Once documents are ingested and intake questions are completed, the user initiates the authoring process. The system generates draft content from source documents, which the user can review and apply. The document can then be saved as a first draft for further review, refinement, and finalization | crf, data science, regulatory submission | source | 4 |
| 203 | Soft Skills to Gain a Competitive Edge in the 21st Century Job Market | Kirk Paul Lafler | Today's economy requires members of the workforce to develop two essential categories of skills: hard skills and soft skills. Hard skills refer to job-specific knowledge and technical abilities that enable individuals to perform specific responsibilities effectively. Examples include SAS, R, Python, and other technical, programming, data analysis, project management, and market research techniques. Soft skills, on the other hand, are less tangible and often difficult to measure. They encompass the personal qualities, attributes, and interpersonal traits that shape how individuals interact and collaborate with others in the workplace. Soft skills are typically developed through life experiences and workplace interactions. The encouraging news is that soft skills can be learned and mastering them provides a significant competitive advantage in today's fast-paced and rapidly evolving job market | source | 4 | |
| 204 | Speeding Up Your Validation Process is As Easy As 1, 2 and 3 | Alice M. Cheng | 'NOTE: No unequal values were found. All values compared are exactly equal. ' Are you excited to see these statements in your SAS output? After laboring for hours, finally, you got a match! Eureka! You finally did it! But before you pop a champagne and celebrate, please note these statements are necessary but not sufficient to a perfect match! In this paper, the author introduces the use of &SYSINFO, an automatic macro variable generated by PROC COMPARE. This single value held by &SYSINFO enables us to know accurately the result of the comparison. The author will then demonstrate how one can take advantages of its features to speed up the validation of numerous deliverables such as datasets, tables, listings and figures, and how one can use EXCEL spreadsheet to make the result more readable. Armed with the techniques, speeding up the validation process is really as easy as 1, 2 and 3. KEYWORDS: PROC COMPARE, Validation, &SYSINFO, Speed Up, Traffic-Lighting, EXCEL |
proc compare, proc step, excel, data cleaning, macro variable, sas log, sysrc, by group processing, adam dataset, data step, ods excel | source | 19 |
| 205 | Stop Guessing. Start Matching. High-Impact SAS PRX Patterns in 20 minutes | Charu Shankar | Messy text is everywhere in clinical programming-IDs embedded in free text, inconsistent visit labels, unpredictable separators, and key-value strings that require precise parsing. Traditional approaches using INDEX, SUBSTR, and FIND often lead to brittle logic, false positives, and code that is difficult to maintain. This paper presents a pattern-driven approach to text processing in SAS using PRX (Perl Regular Expressions). Through three core patterns-validation, extraction, and precise targeting-it demonstrates how PRX enables reliable, scalable, and maintainable solutions. Each pattern is illustrated with concise, production-ready code and practical clinical scenarios. The paper emphasizes how PRX functions return numeric results that SAS interprets in a Boolean context, reinforcing a deeper understanding of SAS logic. Takeaway: cleaner parsing logic, fewer false positives, and reusable patterns that elevate text handling from string manipulation to structured matching | data cleaning, type i error | source | 7 |
| 206 | Strategic AI Coaching for Life Sciences: A Framework for Industry Leaders and Managers | Priscilla Gathoni | As we stand on the cusp of the Fourth Industrial Revolution, leaders are facing a "virtual thunderstorm of change" propelled by the rapid acceleration of Artificial Intelligence (AI). To remain competitive in this high-stakes landscape, the life sciences industry must move beyond traditional management toward a holistic hybrid coaching model. This paper introduces the Farhan-5As framework-a systematic roadmap of Analysis, Architecture, Application, Ascertainment, and Adjustment-designed to harmonize AI-driven efficiency with a necessary company-wide shift in mindset. While technology often triggers a sense of things "falling apart," this framework proves that strategic AI integration actually brings organizational structures together for a greater good. We explore the transformative power of CoachBots and generative AI assistants, which empower leaders to shift their focus from routine technical tasks to high- value social and personal competencies | life sciences, artificial intelligence | source | 15 |
| 207 | Streamlining Workflows in Real-World Evidence Studies with an R-Based Automation Tool | Lihai Song | Routine analyses-summary statistics, regression, survival analysis, meta-analysis, and classification metrics (e.g., F1 score)-are foundational to real-world evidence (RWE) studies but often require repetitive, time-consuming work. We developed an R-based automation tool that increases efficiency, reduces errors, and produces publication-ready outputs. R users incorporate the tool as an R package to standardize syntax, reduce typographical and case-sensitivity errors, and streamline workflows. Non-R users access the same functionality via a Shiny web application that enables point-and-click analysis: upload a dataset, choose variables and analysis type, and generate analysis tables and figures downloadable in multiple formats. The tool simplifies routine RWE analyses, improving productivity and reproducibility across user skill levels | regression analysis, survival analysis, proc means, f1 score, machine learning, nominal variable, competing risks, forest plot, kaplan-meier plot, data cleaning, proc phreg, propensity score | source | 16 |
| 208 | Structure for Success: Delivering a Complex, Accelerated NDA with Evolving Scope | Tingting Tian, Chao Su, Erica Davis |
Late-stage regulatory submissions such as New Drug Applications (NDAs) require tight coordination among clinical, statistical, programming, and other functional teams, while timelines and scope evolve in real time. This paper describes the planning, execution, and lessons learned from an NDA comprising nine components: a supportive Phase II study, four pivotal Phase III studies, three pooling packages, and a combined BIMO package. Approximately 1,300 tables, figures, and listings (TFLs) were produced, with a Safety Update Report (SUR) following. Timelines for each of the Phase III database locks and the target submission date shifted earlier by 2-5 months, reducing development and review cycles | phase iii, data cleaning, efficacy summary, phase ii | source | 4 |
| 209 | Structure-Preserving Preprocessing of Clinical Documents for Large Language Model Analysis | Zun Wang, Juntao Yan |
Clinical trial documents are essential to drug development but are often lengthy and labor intensive to align or compare within or across studies due to strict traceability and governance requirements. Although large language models (LLMs) are increasingly used to improve document analysis efficiency, directly applying LLMs to flat text presents practical challenges. Document length can exceed model context limits, and loss of section boundaries prevents LLMs from recognizing the inherent hierarchical structure of clinical documents, compromising both accuracy and traceability. To address these challenges, this paper introduces a structure-preserving preprocessing workflow that reconstructs clinical documents (e.g., DOCX and PDF files) into an explicit hierarchical representation prior to LLM analysis | clinical trial, analysis set, data science, clinical research | source | 12 |
| 210 | SUPP to NSV: Transforming Data Representation for Improved Reviewer Utility | Soumya Rajesh | Ever since the advent of SDTM standards, non-standard data has been mapped using the vertical structure outlined in Supplemental Qualifier (SuppQual or Supp) datasets. This has caused issues sometimes when the data needs to be merged back with parent domains and used for analysis further down the data reporting process. That's going to change majorly with the release of the new version of SDTM: Model v3.0 and IG v4.0. This paper describes the transformation from vertical to horizontal structure for mapping non-standard datasets to the newer, more efficient Non-Standard Variables (NSV) datasets in SDTM. This would enable the direct integration of non- standard data into General Observation class domains for easier viewing and metadata application, thereby streamlining submissions by reducing structural limitations and promoting consistency within SDTM standards | controlled terminology, data cleaning, cdisc | source | 10 |
| 211 | Swimmer Plots - Some Practical Advice | Ilya Krivelevich | Graphs are an integral part of modern data analysis of clinical trials. Viewing data in a graph along with the tabular results of a statistical analysis can greatly improve understanding of the collected data. Visualized data can very often be the most informative way to understand the insight s from the results. Swimmer plots are an effective graphical presentation of subject status and longitudinal data such as duration of treatment, dose changing, occurrences and durations of events. This type of graph is usually very popular in the early phases of drug development (Phase I / Phase II). An essential objective for medical monitors is to make it possible to visually review when specific medications were administered in response to specific safety and efficacy information throughout the study duration. This visual representation is crucial for tracking patient responses and treatment safety/efficacy. Each subject is represented by an individual horizontal bar (lane) | swimmer plot, adverse event of special interest, adverse events, clinical trial, complete response, standard deviation, data science | source | 11 |
| 212 | Sync & Scale: Empowering Cloud Hub & Team Synergy with SAS Bridge | Mayank Singh | In our clinical industry, efficient and secure data sharing is critical for clinical, regulatory, and research workflows. This paper presents a novel SAS 9.4 macro solution (SAS_S3_SharePoint_DataMover) that automates the transfer of documents/data between Amazon (AWS) S3 (Simple Storage Service) buckets and SharePoint/Teams Online. Leveraging PROC S3 for cloud storage interactions and Microsoft Graph APIs (Application Programming Interface) for SharePoint and Teams, the approach streamlines document management, enhances collaboration, and ensures compliance with industry standards. The SAS_S3_SharePoint_DataMover streamlines the data transfer process by enabling efficient uploading and downloading, thereby reducing manual intervention and decreasing the likelihood of errors. Practical applications illustrate how this integrated workflow facilitates prompt data sharing, expedites review cycles, and ensures robust audit trails |
macro variable, proc step, amazon s3, cloud storage, proc http, macro loop, proc sql, %if-%then-%else, data governance, macro definition | source | 16 |
| 213 | Take CoMmanD of Your Log: Using CMD to Check Your SAS Program Logs | Richann J. Watson | Regardless of the industry, part of writing a SAS program is to ensure that the log is free of any unwanted log messages. When running the program in an interactive SAS session, we can review the log as we execute the program and SAS is good about highlighting ERROR and WARNING messages using colors to draw the eye. Other types of unwanted log messages, such as INFO, uninitialized, character to numeric conversion, may not be so easily spotted. When running the program in batch, each program needs to be opened and scanned for unwanted log message, which is tedious and prone to overlooking a message. There have been several papers illustrating the creation of macros that will check the logs by parsing the logs after the programs have been executed. While these macros are great when you are running a lot of programs for a deliverable and need to check all the logs, these check log macros are not necessarily ideal during development | sas log, elapsed time, sample | source | 12 |
| 214 | Taming Polyglot Analytics: Simplifying Cross-Language Workflows in a Unified IDE | David L. Ward, Troy B. Wolfe |
Modern analytical workflows increasingly span multiple programming languages, each chosen for its strengths in data access, transformation, modeling, or visualization. While this polyglot approach is powerful, it introduces complexity through fragmented tooling, inconsistent workflows, and increased maintenance overhead, ultimately reducing productiv ity and increasing operational risk. This paper demonstrates how cross-language analytic al workflows can be simplified using the Siemens Analytic Workbench , an integrated development environment that enable s analysts to work with multiple programming languages from a single, unified interface. The presentation demonstrates a project in which SAS, Python, R, and SQL are written and execu ted within a single development environment, highlighting both individual and organizational benefits. This paper is intended for statistical programmers and analysts working in increasingly heterogeneous analytical environments | proc sql, statistical programmer | source | 8 |
| 215 | 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 using SAS | 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 |
| 216 | The (ODS) Output of Your Desires: Creating Designer Reports and Data Sets | Louise S. Hadden | SAS procedures can convey an enormous amount of information - sometimes more information than is needed. Most SAS procedures generate ODS objects behind the scenes. SAS uses these objects with style templates that have custom buckets for certain types of output to produce the output that we see in all destinations (including the SAS listing). By tracing output objects and ODS templates using ODS TRACE (DOM) and by manipulating procedural output and ODS OUTPUT objects, we can pick and choose just the information that we want to see. We can then harness the power of SAS data management and reporting procedures to coalesce the information collected and present the information accurately and attractively |
proc contents, proc step, ods output, sas output, estimate, output, excel, data step, proc univariate, odds ratio, proc sql, proc surveyfreq, proc report | source | 12 |
| 217 | The Best Data Dashboard Alternative: More Efficient But Equally Effective Performance Monitoring and Reporting | LeRoy Bessler | You can build a web and email based Performance Monitoring and Reporting package with just Base SAS, ODS Graphics, and ODS HTML5, for an incisive package to make best use of the time, and seize the attention, of its users/recipients. Show Them What's Important! An email, triggered by conditions in the data, can attach (or link to) an Exception Report. It can link to all other relevant information, or you can attach a zip file of the entire package |
time series analysis, sas base | source | 8 |
| 218 | The Evolution of Open-Source Technologies in the Pharmaceutical Industry: Python as a Cost-Effective Solution for Clinical Statistical Programming | Ramesh Potluri | SAS has traditionally been the standard platform for statistical analysis and clinical reporting in the pharmaceutical industry. However, advancements in open -source technologies and evolving regulatory perspectives have driven increased adoption of alternative tools that support automation and advanced analytics. Python offers a powerful and cost-effective solution for automated tool development and machine learning integration. This paper describes the setup of Python and associated development environmen ts, reviews key libraries and functions that enable SAS - equivalent functionality - including data type conversion, sorting, case manipulation, merging, transposing, subsetting, and conditional column creation - and presents practical examples of demographic and adverse event table generation aligned with clinical reporting requirements. AI could generate programming, but programmers who know the language efficiently handle AI | proc step, adverse events, if-then-else, clinical programming, clinical trial, proc freq | source | 9 |
| 219 | The Log Whisperer: Still Reading SAS Logs? Start Ranking Them | Charu Shankar | SAS logs are central to quality control, yet review remains slow, manual, and inconsistent. Programmers scan line by line, triage varies by reviewer, and valuable time is lost separating signal from noise. This paper introduces a SAS-only "Log Whisperer" that transforms log review into a repeatable, data-driven workflow. A lightweight rules table defines what matters. A DATA STEP engine uses PRX pattern matching to detect issues at scale. PROC SQL then scores, ranks, and summarizes logs into a prioritized review queue. Rather than reading everything, reviewers focus immediately on the highest-risk logs and the most common root causes. Takeaway: a reusable SAS utility that reduces QC thrash, improves consistency, and turns log review into a structured, scalable process |
sas log, proc sql | source | 5 |
| 220 | The Next Frontier of Statistical Programming: Vibe Coding with AI Coding Agents into SAS, R & Python | Kevin Lee, Nathan Lee |
The rise of Gen AI is revolutionizing how coding is performed across industry, and "vibe coding" stands at the forefront of this transformation. Coined from Andrej Karpathy's idea of "embracing the vibes" of AI-assisted coding, vibe coding represents a seamless flow between human logic and AI coding agents-where programmers prompt, review, and collaborate with AI to produce code efficiently and intelligently. As an example, Microsoft CEO Satya Nadella estimated in April 2025 that 20% to 30% of Microsoft's code was generated by AI. The presentation explores how vibe coding will reshape programming in Biometrics, where SAS, R, and Python remain essential. It illustrates how conversational AI tools (ChatGPT, Gemini, Claude), AI-native IDEs (Cursor, Windsurf, GitHub Copilot) and customized AI Coding Agents and Agentic workflow enhance traditional workflow - automating coding, debugging, and validation processes while preserving scientific and regulatory rigor | data cleaning, clinical programming, clinical trial, sdtm domain, cdisc | source | 7 |
| 221 | The Open Source Advantage: Powering Innovation in the 21st Century | Kirk Paul Lafler, Ryan Paul Lafler, Joshua J. Cook, Stephen B. Sloan, Anna T. K. Wade |
Organizations worldwide are undergoing a fundamental shift in how software is developed, deployed, and sustained. The rapid emergence of new technologies, combined with the accelerating adoption of open-source solutions - has created an ecosystem where innovation thrives alongside increased complexity. As diverse tools, platforms, and communities converge, organizations must balance opportunity with risk. This presentation examines the benefits, challenges, and emerging opportunities of open-source technologies in the 21st century. Participants will explore how organizations and user communities are addressing critical considerations, including system integration and compatibility, security vulnerabilities, intellectual property concerns, licensing and warranties, and the variability of development and support practices | source | 7 | |
| 222 | The Problems Surrounding Rounding | Jennifer McGrogan, Mario Widel |
Rounding is not a novel concept-examples can be found in ancient civilizations, such as approximations made by the Mesopotamians. Since then, the need for rounding has not diminished, it has rather increased. It was necessary long before the use of computers for numerical calculations and introducing their use has made the rounding process more complex and indispensable. Based on the referenced literature and our own experience, we will show typical problems including: 1. Rounding is unavoidable, for a. table readability, b. validation of results, c. keeping results within reasonable precision, and d. ensuring accurate assignment of CTCAE toxicity grades. 2. Computer representation of numeric results may a. affect number precision, b. be inadvertently rounded, c. introduce calculation errors due to rounding, or d. cause compare differences on numbers that appear identical | decimal, data cleaning, hex., excel, adverse events, ctcae grade | source | 12 |
| 223 | The Tipsy Hangover: Avoiding Indent Headaches in SAS Reports | Lisa A. Mendez, Richann J. Watson |
Creating visually appealing SAS reports shouldn't feel like recovering from a formatting hangover -but when it comes to hanging indents in REPORT procedure, things can get a little... tipsy. In this paper, we serve up a curated cocktail of techniques using a real-world example to help you straighten out those stubborn hanging indents. We'll explore multiple ways to achieve indentation in PROC REPORT using ODS options and clever preprocessing methods. You'll learn why TWIPS (twentieth of a point) matter more than you think, and how understanding them can help you fine-tune your layout with precision. We'll also explain why ODS ESCAPECHAR and the SPLIT option should never share a drink, and how to pre - process variables with line breaks and non-breaking spaces for smoother text flow. As always, we provide a curated list of references to help with other PROC REPORT formatting tips |
trial elements, proc report, ods rtf, sample, proc step, ods pdf, verbatim term, footnote statement, adverse events, mock shell, excel | source | 22 |
| 224 | Three Ways to Over-Engineer Your SAS Custom Steps | Mary Dolegowski, S. Robert Collins |
Custom Steps in SAS Viya offer a way to create reusable, user-friendly code for specific tasks. Custom Steps are created as flows which can include SAS and open source code as well as other Custom Steps or macros. In addition, Custom Steps allow you to create a flexible graphical user interface to guide users when setting parameters and options. This paper illustrates three ways to enhance your Custom Steps by: implementing using hidden reference tables; managing dependencies; custom warnings, errors, and debug logic | lookup, macro variable, macro parameter, macro put, %if-%then-%else, macro quoting function | source | 9 |
| 225 | Timing, Masking, and Resolution: Understanding and Debugging SAS Macros | Carleigh Jo Crabtree | SAS macro processing can produce unexpected results. Having a clear understanding of how and when macro variables are resolved is essential to debugging. This paper examines three foundational concepts essential to writing robust SAS macros: timing, masking, and resolution. Beginning with an overview of the SAS tokenization process and the role of the macro processor, this paper demonstrates how the sequence of macro execution can lead to unintended variable substitution when special characters such as ampersands and percent signs appear within macro variable values. Masking functions %STR and %NRSTR are presented as tools to prevent premature resolution, with attention given to their distinctions and appropriate use cases. This paper further explores how standard macro functions can inadvertently remove character masking during execution, and introduces the "Q" family of macro functions - such as %QUPCASE - as a mechanism for preserving masking through function execution | macro variable, global symbol table, symbol table, macro function, &variable, macro quoting function, macro trigger, where statement, macro definition | source | 11 |
| 226 | Tipping Point Analysis: An Illustration of Sensitivity Analysis on Non-Administrative Censoring for Progression-Free Survival (PFS) | Lihui Deng, Kylie Fan, Xiaoting Qin |
Missing data due to unbalanced non-administrative censoring- such as disproportionate loss to follow- up or unequal rates of treatment discontinuation between study arms can introduce substantial bias in time-to-event clinical trials and compromise the validity of treatment effect estimates. Regulatory agencies emphasize the need for sensitivity analyses to address these concerns, with tipping point analysis serving as a key tool to test result robustness under informative censoring. This paper presents a practical tipping point analysis workflow in a Phase 3, open-label, randomized trial setting comparing an experimental treatment arm to the Standard of Care arm, focusing on progression - free survival (PFS). The approach is model-free and places emphasis on arm imbalance. The process starts by identifying participants who were censored for non-administrative reasons | progression-free survival, sensitivity analysis, treatment effect, clinical trial, phase iii, imputation, adverse events, treatment arm, proc phreg | source | 7 |
| 227 | Tips and Considerations for Preparing Health Data for Efficient and Accurate AI and LLM Modelling | Louise S. Hadden | Large language models (LLMs) and AI systems present new opportunities for pharmaceutical research and clinical insight generation from data derived from electronic medical record systems (EMRs). However, health-related data-especially data streams with open-ended narratives and diverse coding systems-requires rigorous preparation for use in compliant, accurate, and efficient AI workflows. This paper outlines a practical framework for preparing transcoded EMR data for AI models and LLM use within health analytics pipelines. Topics include data cleaning, normalization, de-identification, prompt engineering, and iterative refinement cycles | proc sql, data cleaning, normalization, proc step | source | 5 |
| 228 | TOON Format: A Token-Efficient Data Exchange Solution for AI-Enhanced Clinical Programming | Saikrishnareddy Yengannagari | As pharmaceutical companies increasingly adopt Large Language Models (LLMs) for clinical programming tasks, token-based pricing creates significant cost challenges. A typical CDISC laboratory dataset with 100,000 records generates approximately 10 million tokens in JSON format, resulting in substantial API expenses. This paper introduces TOON (Token-Oriented Object Notation), a compact data format that reduces token consumption by 50-90% compared to JSON while preserving complete SAS metadata including variable labels, formats, and types. We present two open-source SAS macros- %sas2toon and %toon2sas-implemented entirely in BASE SAS requiring no additional licenses. These macros enable seamless bidirectional conversion between SAS datasets and TOON format with 100% round-trip fidelity. Real-world testing demonstrates that a 500-subject ADLB dataset reduced from 5850,000 tokens (JSON) to 920,000 tokens (TOON), representing 80% cost savings per LLM query | clinical programming, dataset, sas base, record count, cdisc | source | 4 |
| 229 | Traceability in Real World trials - just an aERD away. | Ashwini Yermal Shanbhogue | Traceability of data or provenance is an essential requirement for the regulatory review of clinical trial data. Ensuring the ability to trace data from its source to tabulation datasets to analysis results is therefore one of the priorities of a sponsor submitting said data. In a traditional, gold standard, randomized controlled trial (RCT) , where clinical trial data is collected using Case Report For ms (CRFs), one of the ways to ensure and/or demonstrate this traceability is the annotated CRF or aCRF. In recent years however, the increase in availability of Real World Data (RWD) and the evolution of tools available to analyze it has resulted in the rise of a n ew kind of clinical trial - one that utilizes Real World Evidence (RWE) or evidence that is generated from the analysis of RWD, to assess the effectiveness and safety of a therapeutic product. RWD, however, is collected in electronic databases rather than i n CRFs | clinical database, clinical trial, clinical data, cdisc | source | 7 |
| 230 | Transitioning from SAS to R: Implementing Reproducible R Workflows for TLF Validation | Poornima Alavandi | Validation of Tables, Listings, and Figures (TLF) is a critical step in the clinical reporting lifecycle, ensuring accuracy, reproducibility and regulatory compliance. As organizations increasingly adopt R for statistical programming and data analytics, the need for robust and efficient validation workflows within the R ecosystem has grown substantially. This paper presents a comprehensive framework for TLF validation in R, leveraging standardized programming practices, automated comparison tools and reproducible reporting processes. Our approach integrates widely used R packages to build a structured, reproducible system for validating Tables, Listings, and Figures. It supports visual and numerical comparisons and generates validation summaries suitable for regulatory documentation. By adopting a fully scriptable R -based pipeline, the clinical programming teams can enhance efficiency, transparency, and scalability to the validation workflow | data cleaning, proc compare, proc step, missing value, clinical programming, interval variable | source | 17 |
| 231 | Trust but Verify: How ChatGPT and SAS Can Be Comrades! | Steven C. Black | SAS programmers in clinical development are under constant pressure to move faster while upholding uncompromising standards for quality and compliance. ChatGPT introduces a secure, governed large - language-model platform that can help navigate this tension when used thoughtfully. This paper presents practical, real-world examples of how ChatGPT has been applied to SAS programming workflows, including SDTM and ADaM production, derivation logic clarification, TFL output creation, and validation efforts. Particular emphasis is placed on its use within regulated environments, reinforcing reproducibility, traceability, and the continuing necessity of sound programmer judgment. Rather than automating decisions or replacing established roles, ChatGPT is positioned as a trusted comrade-useful, disciplined, and effective-working alongside SAS programmers without taking anyone's job hostage | data cleaning, sas programmer, sas programming, cdisc | source | 3 |
| 232 | Trusting Your R Packages: A Practical, Risk-Based Approach to External Package Validation | Radhika Etikala, Valeria Duran |
The increasing use of R in regulated clinical research environments introduces new challenges for ensuring reliability, reproducibility, and traceability of analytical workflows. While internally developed R packages can be validated against formal specifications, external packages present unique challenges due to their open-source nature, varying development practices, and complex dependency structures. This paper presents a practical, risk-based framework for Performance Qualification (PQ) of external R packages within a validated scientific computing environment. The proposed Risk -Based Performance Qualification (RPQ) approach leverages existing repository checks, package metadata, and automated testing evidence to provide structured, scalable assessment of package reliability. Rather than attempting full validation of all external packages and dependencies, RPQ focuses on generating transparent evaluation outputs that support downstream decision- making | data cleaning, out=, primary endpoint, r package | source | 14 |
| 233 | Two Approaches to Phase-Specific TRTEMFL in ADAE: A Neoadjuvant-Adjuvant Case with Surgery Between Phases | Youlan Shen, Leah Suttner |
Most ADAE implementations assume a single treatment phase. The standard TRTEMFL (treatment- emergent adverse event flag) derivation works well in single treatment phase setting, but it does not provide the flexibility to attribute adverse events (AEs) to distinct clinical phases (e.g., neoadjuvant vs adjuvant). In multi-phase trials, treatments, mechanisms, and risk windows often differ by phase; merging all AEs under a single TRTEMFL obscures phase-specific risk. This paper uses a neoadjuvant-surgery-adjuvant oncology case to show how to define TRTEMFL in a multi-phase study without duplicate AE occurrence in each phase. The paper proposes two specification approaches for separating treatment-emergent AEs by phase: (1) assign APERIOD and derive a single TRTEMFL; and (2) derive phase-specific flags (TREM0xFL) alongside an overall TRTEMFL. Both approaches support phase-specific AE tables | adverse events, serious adverse event | source | 6 |
| 234 | Two Paths, One PK Journey: The Art of Balancing ADPC and ADNCA | Ashok Abburi, Rakhe Jacob, Shibani Harite |
As CDISC adoption increases and regulatory expectations evolve, selecting the right analysis dataset for pharmacokinetic (PK) work has become a practical decision point. ADPC is commonly used as an analysis- friendly repository for PK concentration data and internal review outputs. ADNCA is a CDISC ADaM dataset designed specifically to support non-compartmental analysis (NCA) and submission traceability. This paper compares ADPC and ADNCA with a focus on their intended use, structural differences, and implic ations for regulatory delivery. Using workflow examples, it highlights how ADNCA can improve traceability and metadata documentation (e.g., define.xml/ADRG), while ADPC remains useful for exploratory analysis and concentration-time reporting | key, time variable, compartmental analysis, pk parameter, clinical programming, sample, data cleaning, adam dataset, cdisc | source | 7 |
| 235 | 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 |
| 236 | Unleash the R-volution: A Blueprint for Building Package Validation Capabilities in our own organization | Kevin Lee | As the use of R continues to expand in clinical trial programming, ensuring that R packages are validated for regulatory compliance, reproducibility, and traceability has become essential. This presentation provides a step-by- step blueprint for building robust R package validation capabilities within your organization. We will begin by clarifying what R and R packages are such as base and recommended packages maintained by the R Core Team to community-contributed and in-house packages. The session then focuses on why validation matters, emphasizing principles such as accuracy, reproducibility, and data integrity to meet FDA and EMA expectations for reliable, auditable results. A central part of the presentation introduces a risk-based validation framework that classifies packages according to their purpose, maintenance quality, community usage, and testing rigor | data cleaning, clinical trial, clinical programming | source | 5 |
| 237 | Unleashing Open-Source Potential in SAS: The PharmaForest Ecosystem (proc pharmaforest data=open_source out=... ;) | Sharad Chhetri, Ryo Nakaya |
The recent surge in the use of R and open-source software in drug development has sparked a powerful movement focused on shared knowledge, transparency, flexibility, and community -driven innovation. Notably, this shift has produced an unexpected-but positive-side effect: it has inspired many long-time SAS users to adopt the same spirit of openness and community sharing of SAS codes which they have developed. Increasingly, SAS professionals are recognizing the strength of collective knowledge and the value of contributing their own work as open-source code. Emerging from this momentum is PharmaForest -a unified repository of SAS packages built upon the SAS Packages Framework (SPF) (https://github.com/yabwon/SAS_PACKAGES )1. This initiative is dedicated to accelerating open collaboration among SAS users and cultivating a vibrant, sustainable community where ideas, code, and best practices can flourish together -like a forest ecosystem | sas packages, clinical programming, data cleaning, clinical research, pharmaceutical industry | source | 7 |
| 238 | User-defined Functions for Programming Population PK and PKPD Datasets | Zhongqing He, Man Li, Hong Yan |
The Population PK (ADPPK) and Pharmacodynamic (ADPPKPD) datasets are the analysis-ready CDISC compliant datasets for population PK and Exposure-Response modeling analysis. These datasets are longitudinal, consist of various data elements with complexity across the clinical database domains, and involve many derivations in covariates (baseline and/or time-varying) in addition to dosing, PK, and PD, which often require using different calculations for a given covariate, or different covariates to facilitate different analyses during model development and selection process. A set of user-defined functions was developed to reduce cycle time for dataset development. We will share several key functions which have been proven to be easy to use, accurate, and efficient in pharmacometrics data programming and output generating | population pk, data wrangling, proc step, cdisc | source | 7 |
| 239 | Using Large Language Models to Validate TLF Outputs Against Statistical Review Comments: An End-to-End Python Framework | Kishore Reddy Rollakanti | Quality control of clinical Tables, Listings, and Figures (TLFs) requires verifying that statistical review comments are fully and correctly addressed in the final output process that is typically manual, time- consuming, and susceptible to inconsistency. Large studies often involve hundreds of review items, multiple programming revisions, and extensive output packages, making it challenging to ensure that each comment has been resolved and documented consistently. This paper presents an end-to-end Python framework that leverages Large Language Models (LLMs) to automate validation of statistical review comments against final TLF outputs. The solution combines structured review metadata (output identifier, review finding, and programmer resolution) with an EXCEL tracker with automated RTF-to-text extraction from individual output files packaged as a zip archive |
excel, ods rtf, data cleaning | source | 10 |
| 240 | Virtual Data, Real Standards: Leveraging Data Simulation for Smarter Clinical Trials | Sangeeta Shabadi, Nitesh Patil, Jonathan Henshaw |
The Study Data Tabulation Model (SDTM) is central to standardized clinical trial data submissions across global regulatory agencies. While traditionally focused on data organization and compliance, SDTM emerged as a strategic tool for simulation in clinical trial analysis. As clinical data grows in volume and complexity, automation becomes critical. SDTM simulation delivers fast, flexible creation of CDISC- compliant synthetic datasets; customized for any study design or therapeutic area. This paper explores how data simulation in SDTMs involves generating synthetic SDTM-like datasets using random sampling and domain-specific rules to mimic real clinical data. Simulation techniques were used to create realistic subject profiles, dosing patterns, adverse events, and lab values, enforcing cross-domain logic and SDTM structural requirements | clinical data, clinical trial, adverse events, data cleaning, sdtm programming, controlled terminology, vital signs, sample, sdtm domain, clinical programming, cdisc | source | 16 |
| 241 | Visualizing PK and ADA Data at Scale: A Parameter-Driven SAS Macro for Box Plot Generation | Prasannanjaneyulu Narisetty, Anilkumar Anksapur |
Effective visualization of Pharmacokinetic (PK) and Anti-drug Antibody (ADA) data is vital for interpreting complex clinical trial data. This presentation introduces a dynamic, user-friendly SAS macro designed to generate high-quality box plots for Immunogenicity analyses, with a particular focus on ADA evaluations. The macro creates two different visualizations: 1. Antibody Titer by Study Visit 2. ADA Sample Result vs. Predose Serum Concentration by Study Visit and ADA Status This paper demonstrates how the macro generates two plots through extensive, parameter-driven customization. Users can define the analysis population and input datasets, apply filters, specify X and Y axis variables, incorporate ADA status, and add statistical summaries. The macro verifies that the specified X and Y variables exist in the input dataset and ensures that color specifications remain consistent with data groupings | box plot, analysis set, data cleaning, visit, sample, in=, macro definition, proc sgplot | source | 9 |
| 242 | Voice-driven Data Science: Real-Time Data Analysis with R | Philip Bowsher | ggbot2 is a new R package for voice assisted analysis with ggplot2. This paper will introduce it and show how statistical programmers can get up and running with it easily. By leveraging OpenAI's Realtime API and Shiny, users can generate ggplot2 visualizations through natural language commands. This is an experiment in the possibility of this tool in drug development as voice driven support takes shape. I | proc sgplot, bar chart, proc step | source | 16 |
| 243 | When Code Isn-t Enough: Communication and Leadership Skills for Statisticians and Programmers | John M. LaBore, Joshua M. Horstman, Robert Goodloe |
Statisticians and statistical programmers working in SAS and R and other software are entering a period of rapid change, driven by automation and Artificial Intelligence (AI)-assisted development. While these tools can generate code and accelerate analyses, they cannot replace the human skills that turn results into decisions and insights into action. As technical barriers continue to fall, communication and leadership skills are becoming the primary differentiators for career growth. Whether you aspire to be a trusted technical expert, lead analytics teams, or expand your influence as a consultant, your ability to clearly explain results, defend analytical choices, and guide others now matters more than ever. In an AI- driven world, those who can communicate with confidence and lead with clarity will stand out. Toastmasters International offers a practical, supportive way for technical professionals to deliberately build these skills | statistical programmer, artificial intelligence | source | 6 |
| 244 | Why Standards Matter More Than Code in the Age of GenAI | Bhavin Busa | The growing adoption of generative artificial intelligence (GenAI) in statistical programming and clinical reporting has introduced new opportunities for automation, alongside concerns related to reliability, reproducibility, and regulatory acceptability. While large language models (LLMs) are inherently probabilistic, their outputs can become significantly more consistent and predictable when guided by structured, machine-readable specifications. CDISC Analysis Results Standard (ARS), together with emerging Analysis Concepts (AC) and Derivation Concepts (DC), provides a formalized framework for representing analysis intent, derivation logic, and output definitions. This paper demonstrates how ARS-based metadata can be used as controlled input to LLMs to enable rapid and reliable generation of Tables, Figures, and Listings (TFL) programs in SAS and | clinical programming, data cleaning, statistical programmer, cdisc | source | 6 |
| 245 | Women Leadership in AI-Driven Clinical Programming: Navigating Intersectionality and Innovation | Lida Gharibvand | Background. The accelerating integration of artificial intelligence (AI) and machine learning (ML) into clinical programming is increasingly restructuring the pharmaceutical research process and workforce. Yet the convergence of three relevant forces - women's persistent underrepresentation in clinical-trial leadership, the algorithmic turn in trial design and analytics, and the intersectional barriers facing women of color - has not been quantitatively synthesized. Objectives. We synthesize the evidence across four interrelated factors: (i) the magnitude of women's underrepresentation as principal investigators (PIs) and senior authors; (ii) the scope and maturity of AI/ML adoption in clinical programming; (iii) how intersectional factors moderate gender-leadership disparities; and (iv) the empirical linkage between leadership diversity and innovation outcomes. A pre-specified subgroup meta-analysis is performed to quantify between-group heterogeneity. Methods | clinical programming, regression analysis, clinical trial, prediction interval, subgroup analysis, sensitivity analysis, effect size, clinical research, confidence interval, data science | source | 31 |
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