YuTing Tian - An Introduction to Creating Standardized Clinical Trial Data with SAS

The correct bibliographic citation for this manual is as follows: Case, Todd and YuTing Tian. 2022. An Introduction to Creating Standardized Clinical Trial Data with SAS. Cary, NC: SAS Institute Inc. An Introduction to Creating Standardized Clinical Trial Data with SAS Copyright 2022, SAS Institute Inc., Cary, NC, USA ISBN 978-1-955977-90-6 (Hardcover) ISBN 978-1-955977-95-1 (Paperback) ISBN 978-1-955977-96-8 (Web PDF) ISBN 978-1-955977-97-5 (EPUB) ISBN 978-1-68580-026-0 (Kindle) All Rights Reserved. Produced in the United States of America.

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Other brand and product names are trademarks of their respective companies. SAS software may be provided with certain third-party software, including but not limited to open-source software, which is licensed under its applicable third-party software license agreement. For license information about third-party software distributed with SAS software, refer to http://support.sas.com/thirdpartylicenses . Contents About This Book What Does This Book Cover? The purpose of this book is to introduce standardized clinical trial data to anyone interested in understanding the pharmaceutical industry and how that data is collected and created. This book introduces the concept of standardized clinical data, technical terms, and programming practices in the pharmaceutical industry as well as clear and concise explanations with numerous practical examples. We include basic knowledge of the pharmaceutical industry as well as SAS programming practices used in the industry.

This book does not cover how to create define.xml, although we do introduce it to the reader. What Are the Prerequisites for This Book? The only prerequisite for this book is an interest in the pharmaceutical industry. What Should You Know about the Examples? This book includes SAS code and simulated data for the reader to gain hands-on experience standardized clinical data. Visit the author's page at http://support.sas.com/case to access theexample code and data. Software Used to Develop the Books Content SAS Version 9.4 was used to develop the content and examples in this book. Example Code and Data This book includes data and complete programs used to create simulated standardized clinical trial data.

Visit http://support.sas.com/case to access theexample code and data. An example to derive sex in the Demographics domain is demonstrated below: /*Derive SEX*/ if SEX_= "Female" then SEX= "F" ; else if SEX_= "Male" then SEX= "M" ; else if SEX_= "Unknown" then SEX= "U" ; else if SEX_= "Undifferentiated" then SEX= "UNDIFFERENTIATED" ; SAS OnDemand for Academics This book is compatible with SAS OnDemand for Academics. If you are using SAS OnDemand for Academics, then begin here: https://www.sas.com/en_us/software/on-demand-for-academics.html . Acknowledgments Thank you to CDISC and the technical reviewers who provided feedback: Margaret Hung, Matt Becker, Peter Eberhardt, Laura Elliott, William Kuan, and Crystal Cheng. We Want to Hear from You SAS Press books are written by SAS Users for SAS Users. We welcome your participation in their development and your feedback on SAS Press books that you are using.

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Chapter 1: Understanding the Industry In the pharmaceutical industry, there is a mandate to create standardized clinical data using very specific rules. These rules are created and governed by the Clinical Data Interchange Standards Consortium (CDISC). In this book, we describe and illustrate how to create these required CDISC data sets with SAS code. A statistical programmer should be familiar with the CDISC rules required to create standardized clinical trial data sets. After reading this book, readers will be able to understand CDISC standardized clinical data structures, as well as how to create it. 1.1 Statistical Programmer Work Process In the pharmaceutical industry, the primary goal of a statistical programmer is to create standard data efficiently in order for the clinical trial biostatistician to perform their analysis.

A simplified illustration of the process workflow for the statistical programmer is shown in . shows that the work process starts from the Case Report Form (CRF), which is designed for a specific study to collect clinical trial raw data from a site. Often, studies are global having sites in countries all over the world. The Data Management group creates the CRF by working with the statistical programmer and other functions to ensure that the appropriate data is collected for the purpose of that study. After the CRF is created and data is entered into it by the sites, the statistical programmer uses this data to create CDISC Study Data Tabulation Model (SDTM) domains to group collected information from the CRF in a way that facilitates standardization. The statistical programmer then creates CDISC Analysis Data Model (ADaM) data sets from the SDTM domains to support clinical trial analysis.

Figure 1.1: Statistical Programmer Process Workflow Note: When we refer to SDTM, we use the term domain, and for ADaM, we use the term data set. To be crystal clear, both models generate standardized clinical data using SAS. Creating SDTM and ADaM data sets ensure that data will meet the criteria to be accepted by regulatory agencies such as the United States Food and Drug Administration (FDA). Finally, the statistical programmer generates the Tables, Figures, and Listings (TFLs), which are used to support analysis presented in the Clinical Study Report (CSR). The CSR is used to provide evidence to regulatory agencies about the safety and efficacy of the study drug. Note: This workflow actually represents a much more complicated process.

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