Validating clinical trial data reporting with sas

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If you don't understand how the data is arranged, the values that are reasonable for each variable, and the way the data should behave, you cannot ensure that the final result of your programming effort is complete or even appropriate.Therefore, to be a successful programmer in the pharmaceutical industry, you need to understand validation requirements and to learn how to make the code do the bulk of the work so that your programs are efficient as well as accurate.Brian is an adjunct faculty member at Philadelphia University and Arcadia University in Philadelphia, PA.The program addresses good programming practices, common clinical data sets, reporting with data _null, and SAS macros."A delightful introduction to the field of clinical data validation and reporting!Concepts and techniques are gently presented with hands-on examples and accompanying SAS code.

If you haven't studied a mathematical or statistical (or similar) course at university it is unlikely you will have heard of SAS until you have a job in which SAS is required as a tool for that particular role.Preface ix Acknowledgments xi Pharmaceutical Industry Overview 1Introduction 2Regulations 2Health Insurance Portability and Accountability Act 2The Code of Federal Regulations 3Guidance for Industry 4International Conference on Harmonisation of Technical Requirements 5Clinical Data Interchange Standards Consortium 6Documentation 7Standard Operating Procedures 7Companywide Standard Operating Procedures 7Department Standard Operating Procedures 8Task Standard Operating Procedures 8SAS Programming Guidelines 9Quality Control versus Quality Assurance 9Patient versus Subject 10Conclusion 10Validation Overview 11Introduction 12Validation versus Verification 12Why Is Validation Needed?13Presenting Correct Information 13Validating Early Saves Time 13Developing a Positive Relationship 14How Do You Approach Validation?The reader will appreciate the comprehensive review of important terminology such as CRF, e CTD, CDISC, ADa M, SDTM, SOP, SAP, and TLF.Actual mock-up tables for Safety, Efficacy, and Laboratory data are provided, along with a QC Checklist and Statistical Analysis Plan.

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