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Understanding EC vs. EX Domains in SDTM: When to Use Each Understanding EC vs. EX Domains in SDTM: When to Use Each In SDTM, the EC (Exposure as Collected) and EX (Exposure) domains are both used to capture data related to drug or therapy exposure, but they serve different purposes depending on how the exposure data is collected and whether the study is blinded or unblinded. Key Updates from PharmaSUG Papers: PharmaSUG 2017 Paper DS08 introduces the historical context of the EC domain, which was established in SDTMIG v3.2 to support the EX domain by providing detailed traceability for exposure data. EC helps capture deviations, titrations, and other variations from planned dosing, especially when the collected data doesn't match protocol-specified dosing. PharmaSUG 2022 Paper DS121 emphasizes the importance of capturing dose modifications using the EC domain, which often occurs in oncology trials. By utilizing EC, sponsors can accurate...

Study Start Date in SDTM – Why Getting It Right Matters Study Start Date in SDTM – Why Getting It Right Matters The Study Start Date (SSTDTC) is a crucial element in the submission of clinical trial data, especially in meeting regulatory requirements. Since December 2014, the FDA has provided explicit guidance on defining and utilizing this data point, but many sponsors and service providers face challenges in its consistent application. Missteps in defining the Study Start Date can lead to technical rejection during submission reviews, delaying the regulatory process. This article explores the definition, importance, and proper implementation of the Study Start Date in SDTM (Study Data Tabulation Model) submissions, based on regulatory guidance and best practices. FDA’s Definition of Study Start Date The FDA, in its 2014 guidance, clarified that the Study Start Date for clinical trials is the earliest date of informed consent for any subject ...

Best Practices for Joining Additional Columns into an Existing Table Using PROC SQL Best Practices for Joining Additional Columns into an Existing Table Using PROC SQL When working with large datasets, it's common to add new columns from another table to an existing table using SQL. However, many programmers encounter the challenge of recursive referencing in PROC SQL when attempting to create a new table that references itself. This blog post discusses the best practices for adding columns to an existing table using PROC SQL and provides alternative methods that avoid inefficiencies. 1. The Common Approach and Its Pitfall Here's a simplified example of a common approach to adding columns via a LEFT JOIN : PROC SQL; CREATE TABLE WORK.main_table AS SELECT main.*, a.newcol1, a.newcol2 FROM WORK.main_table main LEFT JOIN WORK.addl_data a ON main.id = a.id; QUIT; While this approach might seem straightforward, it leads to a warning: "CREATE T...

Comprehensive SDTM Review Mastering the SDTM Review Process: Comprehensive Insights with Real-World Examples The process of ensuring compliance with Study Data Tabulation Model (SDTM) standards can be challenging due to the diverse requirements and guidelines that span across multiple sources. These include the SDTM Implementation Guide (SDTMIG), the domain-specific assumptions sections, and the FDA Study Data Technical Conformance Guide. While automated tools like Pinnacle 21 play a critical role in detecting many issues, they have limitations. This article provides an in-depth guide to conducting a thorough SDTM review, enhanced by real-world examples that highlight commonly observed pitfalls and solutions. 1. Understanding the Complexity of SDTM Review One of the first challenges in SDTM review is recognizing that SDTM requirements are spread across different guidelines and manuals. Each source offers a unique perspective on compliance:...

Revolutionizing SDTM Programming in Pharma with ChatGPT Revolutionizing SDTM Programming in Pharma with ChatGPT By Sarath Introduction In the pharmaceutical industry, standardizing clinical trial data through Study Data Tabulation Model (SDTM) programming is a critical task. The introduction of AI tools like ChatGPT has opened new opportunities for automating and enhancing the efficiency of SDTM programming. In this article, we will explore how ChatGPT can assist programmers in various SDTM-related tasks, from mapping datasets to performing quality checks, ultimately improving productivity and accuracy. What is SDTM? SDTM is a model created by the Clinical Data Interchange Standards Consortium (CDISC) to standardize the structure and format of clinical trial data. This model helps in organizing data for submission to regulatory bodies such as the FDA. SDTM programming involves mappi...

Unleashing the Power of PROC DATASETS in SAS Unleashing the Power of PROC DATASETS in SAS The PROC DATASETS procedure is a versatile and efficient tool within SAS for managing datasets. Often described as the "Swiss Army Knife" of SAS procedures, it allows users to perform a variety of tasks such as renaming, deleting, modifying attributes, appending datasets, and much more, all while consuming fewer system resources compared to traditional data steps. In this article, we’ll explore key use cases, functionality, and examples of PROC DATASETS , illustrating why it should be part of every SAS programmer's toolkit. 1. Why Use PROC DATASETS? Unlike procedures like PROC APPEND , PROC CONTENTS , and PROC COPY , which focus on specific tasks, PROC DATASETS integrates the functionalities of these procedures and more. By using PROC DATASETS , you avoid the need for multiple procedures, saving both time and system resources since it on...

Advanced SDTM Mapping Pitfalls and How to Avoid Them Advanced SDTM Mapping Pitfalls and How to Avoid Them Introduction Mapping clinical data to SDTM domains is a complex process involving many technical and logical challenges. For experienced programmers, common issues often revolve around proper handling of controlled terminology, managing derived variables, ensuring consistency between domains, and maintaining relational integrity. This article explores some of the most common SDTM mapping pitfalls, with advanced solutions and SAS code examples, to help avoid regulatory submission errors. Pitfall 1: Handling Derived Variables Incorrectly One of the most common issues in SDTM mapping is incorrectly handling derived variables, which can lead to inaccuracies in key datasets such as EX (Exposure) and VS (Vital Signs). Example: A derived variable such as EXDUR (Exposure Duration) in the EX domain may not be properly calculated or m...