As clinical research professionals, we often grapple with a unique challenge: transforming our forward-looking protocol documents into retrospective study documentation. One particular area where this becomes crucial is in the Clinical Study Data Reviewer's Guide (cSDRG), especially when documenting the Study Design section. Today, we'll explore why simply copying and pasting from your protocol isn't the best approach, and how to effectively translate your study design documentation into its proper historical context. Why Time Matters in Clinical Documentation The protocol and cSDRG serve fundamentally different purposes in the clinical research narrative. Your protocol is your roadmap - it outlines what you plan to do. The cSDRG, on the other hand, tells the story of what actually happened. This distinction is crucial for regulatory reviewers who need to understand how your study unfolded in reality. ...

SDTM Dataset Structure Documentation: A Senior Programmer's Perspective Introduction: Why I'm Writing This After spending over 15 years mapping clinical data to SDTM, I've seen firsthand how proper dataset structure documentation can make or break a submission. Recently, I encountered a situation where incomplete structure descriptions in Define.xml led to significant rework in a late-phase study. This experience prompted me to share my insights on why meticulous documentation of dataset structures is crucial. The Real-World Impact of Structure Documentation Let me share a recent example from my work. We inherited a study where the LB domain structure was documented simply as: "One record per analyte per planned time point per visit per subject" However, the key variables included: ...

Mastering the Art of Comments in Define.xml: Your Ultimate Guide to Clinical Data Documentation Posted by Sarath In the world of clinical data management, the define.xml file serves as the cornerstone of dataset documentation. While most professionals focus on the basic structural elements, the Comments tab often remains an underutilized goldmine of information. Today, we'll dive deep into how to leverage this powerful feature to enhance your clinical data documentation. Quick Takeaway: Well-crafted comments in your define.xml can significantly reduce queries during regulatory submissions and streamline the review process. Why Comments Matter in Define.xml The Comments tab isn't just an afterthought - it's your opportunity to provide crucial context that doesn't fit neatly into other standardized fields. Think of i...

By StudySAS Team | January 7, 2025 Introduction to SDTM Domains In clinical data management, the Study Data Tabulation Model (SDTM) is a crucial standard for organizing and formatting data to streamline regulatory submissions. Among its various domains, the EX (Exposure) and EC (Exposure Events) domains play significant roles in documenting participant exposures and related events during a study. This blog post delves into when and how to annotate these domains, providing detailed examples and best practices based on the SDTM Implementation Guide (IG) Version 3.3 . Understanding the EX Domain The EX (Exposure) domain is essential for capturing detailed information about the administration of investigational products, concomitant medications, or other exposures participants receive during a clinical study. ...

Key Concept: The decision to map protocol version information in SDTM Disposition (DS) domain requires careful analysis of its relationship to disposition events and understanding of data management requirements. Understanding Protocol Version's Role in Disposition Events Protocol versions can significantly impact disposition events in clinical trials. Their relationship to these events determines the appropriate mapping strategy within the SDTM structure. This relationship can be categorized into two main types: Direct Impact Relationship When protocol version changes directly cause or influence disposition events, such as: Subject withdrawal due to protocol amendment modifications Study discontinuation resulting from significant protocol changes Protocol-mandated subject transfers between treatment arms Contextual Relationship When proto...

In the world of clinical trials, data management and standardization play crucial roles in ensuring the quality and integrity of research outcomes. One particularly nuanced aspect of this process is the proper assignment of EPOCH values in SDTM (Study Data Tabulation Model) datasets, especially when dealing with pre-consent data. What is an EPOCH? An EPOCH in clinical trials represents a distinct period within the study's planned design. It helps organize and contextualize various events, interventions, and findings that occur during the study. Common EPOCHs include SCREENING, TREATMENT, FOLLOW-UP, and others as defined by the study protocol. Essential EPOCH Characteristics: It is a standardized way to identify different phases of a study Each EPOCH represents a planned element of the study design EPOCHs help establish temporal relationships between different study e...

The Critical Role of ODS LISTING Close Statements in SAS: Avoiding Comment Width Errors One of the common challenges SAS programmers face is encountering the error message: "ERROR: Comment width is not between 1 and 200 characters." This error, while seemingly straightforward, can be particularly frustrating when it appears unexpectedly in your SAS log. In this article, we'll dive deep into understanding this error and how proper management of ODS LISTING statements can help you avoid it. Introduction to ODS The Output Delivery System (ODS) in SAS enables users to control the appearance and destination of output generated by SAS procedures. ODS allows output to be directed to various destinations like HTML, PDF, RTF, and the default Listing destination. Proper management of these destinations is critical to ensuring clean output and avoiding errors. Understanding the Error The error message regarding commen...

Power Up Your Data Cleaning with the SAS COMPRESS Function When handling large datasets in SAS, it's common to encounter unwanted characters, extra spaces, or other clutter that can hamper your data analysis. Fortunately, the COMPRESS function helps you clean up your text data efficiently. It can remove, or even keep, specific characters from your strings with minimal effort. Keep reading to learn how you can harness the full potential of the SAS COMPRESS function. 1. Quick Overview of the COMPRESS Function The COMPRESS function in SAS removes (or optionally keeps) certain characters from a character string. Its basic syntax looks like this: result_string = COMPRESS(source_string ); source_string : The original string you want to modify. characters_to_remove (optional): A list of specific characters to eliminate. modif...

Solving Non-Printable Characters in AETERM/MHTERM for SDTM Datasets Solving Non-Printable Characters in AETERM/MHTERM for SDTM Datasets Managing text variables in SDTM domains such as AETERM (for Adverse Events) or MHTERM (for Medical History) can be challenging when non-printable (hidden) characters sneak in. These characters often arise from external data sources, copy-pasting from emails, encoding mismatches, or raw text that includes ASCII control characters. In this post, we’ll explore methods to detect and remove these problematic characters to ensure your SDTM datasets are submission-ready. 1. Identifying Non-Printable Characters Non-printable characters generally fall within the ASCII “control” range: Hex range: 00 – 1F and 7F Decimal range: 0 – 31 and 127 In SAS, you can detect these characters by examining their ASCII values using RANK() , or by leveraging built-in functions like ANYCNTRL() . ...

Quick Tip: See SAS Dataset Labels Without Opening the Data Quick Tip: See SAS Dataset Labels Without Opening the Data When working with SAS datasets, checking the dataset label without actually opening the data can be very useful. Whether you're debugging or documenting, this small trick can save you time and effort! 1. Use PROC CONTENTS PROC CONTENTS is the most common and straightforward way to view the dataset label. proc contents data=yourlib.yourdataset; run; The dataset label will appear in the output as the field: Data Set Label . 2. Query DICTIONARY.TABLES or SASHELP.VTABLE For a programmatic approach, use the DICTIONARY.TABLES table or SASHELP.VTABLE view to query dataset metadata. Example Using PROC SQL proc sql; select memname, memlabel from dictionary.tables where libname='YOURLIB' and memname='YOURDATASET'; quit; ...

Detecting Non-Printable Characters in SDTM Datasets Using SAS Non-printable characters in datasets can lead to errors and inconsistencies, especially in the highly regulated environment of clinical trials. This blog post demonstrates how to create a SAS program that identifies non-printable characters in all SDTM datasets within a library and generates a comprehensive report. Why Detect Non-Printable Characters? Non-printable characters, such as ASCII values below 32 or above 126, can cause issues during data validation, regulatory submissions, and downstream processing. Detecting them early ensures the quality and compliance of your SDTM datasets. The SAS Program The following SAS program processes all SDTM datasets in a library and generates a combined report of non-printable characters, including: Dataset name : The dataset where the issue exists. V...

SDTM aCRF Annotation Checklist SDTM aCRF Annotation Checklist By Sarath Annapareddy Introduction Creating an SDTM Annotated Case Report Form (aCRF) is a critical step in clinical trial data submission. It ensures that data collected in the CRF maps correctly to SDTM domains, adhering to regulatory and CDISC standards. This checklist serves as a guide to creating a high-quality SDTM aCRF ready for regulatory submission. 1. General Formatting Ensure the aCRF uses the latest SDTM IG version relevant to the study. The document should be clean, legible, and free of overlapping annotations. Page numbers in the aCRF should align with the actual CRF pages. Annotations must be in English, clear, and consistently for...

Common P21 SDTM Compliance Issues and How to Resolve Them Common P21 SDTM Compliance Issues and How to Resolve Them By Sarath Annapareddy Introduction Pinnacle 21 (P21) is a cornerstone for validating SDTM datasets against CDISC standards. Its checks ensure compliance with regulatory requirements set by the FDA, PMDA, and other authorities. However, resolving the issues flagged by P21 can be challenging, especially for beginners. This post dives into common P21 compliance issues and provides actionable solutions with examples. 1. Missing or Invalid Controlled Terminology Issue: P21 flags variables like LBTESTCD or SEX as non-compliant with CDISC Controlled Terminology (CT). This happens when values in your datasets are o...

Define.xml and cSDRG QC Checklist for FDA and PMDA Submissions Comprehensive QC Checklist for Define.xml and cSDRG Ensuring Quality and Compliance for FDA and PMDA SDTM Submissions Introduction The Define.xml and Clinical Study Data Reviewer’s Guide (cSDRG) are critical components of SDTM submissions to regulatory agencies like the FDA and PMDA. These documents help reviewers understand the structure, content, and traceability of the datasets submitted. A robust QC process ensures compliance with agency requirements, minimizes errors, and enhances submission success. This blog outlines a detailed manual QC checklist for both Define.xml and cSDRG, emphasizing key differences between FDA and PMDA requirements. Define.xml QC Checklist 1. Metadata Verification Verify all datas...