Validating clinical trial data reporting

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?

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14Start with All the Information 14Have a Plan 15Make the Code Do the Work 16Ask Questions 16Be Proactive 16Validation Must Come First 17Validation Methods 17Independent Programming 18Peer Review 19Validation Checklists 20Software Development Life Cycle 21Conclusion 22Documentation and Maintenance 23Introduction 24Starting the Process 25Study Protocol 25Annotated Case Report Form 26Statistical Analysis Plan 29Meeting Minutes 31Internal Program Documentation 32Program Header 32Body Comments 34Output Titles 35External Documentation 35Data Definition Tables 35Program Directory 36Validation Files 37Make Programs Maintainable 38Create and Follow Naming Conventions 38Make It Easy to Read 38One Program, One Purpose 42Comments, Comments, Comments 43Use Macros Judiciously 44Make Data Maintainable 44Order Your Data 44Label Everything 49Attach Formats Sparingly 50Consistency Is Key 51Good Housekeeping 51Look-but Don't Touch 53Conclusion 56General Techniques to Facilitate Validation 57Introduction 58Validation Tools 58Procedures 58SAS Options and Language Elements 67Using Macros Effectively 72Techniques That Facilitate Validation 80Start with a Clean Log 80Print Only What You Need-When You Need It 81Tracking Problems 82Using PROC TRANSPOSE or an Alternative Solution 85Tracking Dropped Data 89Conclusion 93Data Import and Export 95Introduction 96Validating the Import Process 96Validating the Export Process 98General Items to Watch For When Transferring Data 99Working with SAS Files 100SAS Data Sets 100SAS Transport Files 101Working with Other File Types 102Microsoft Excel Files 102Flat Files 103Common Procedures Used for Validating Data Transfers 104Proc Contents 104Proc Compare 108Conclusion 112Common Data Types 113Introduction 114Study Populations 114Safety 115Intent-to-Treat 115Efficacy 116Common Data Domains 116Subject Demographics 116Inclusion/Exclusion Criteria 117Subject Disposition 118Medical History 118Physical Examination 120Vital Signs 120Treatment Exposure 122Concomitant Medications 123Adverse Events 124Clinical Laboratory Data 126Conclusion 137Reporting and Statistics 139Introduction 140Pre-Output Validation Steps 140Code Review 140Log Review 141Output Validation Steps 142Understanding the Data 142Understanding the Output 143Checking the Result 143Cross-Checking Related Output 146Checking the Cosmetics 153Updating the Specifications 157Keeping What Is Important 157Final QC Steps 158Conclusion 158Sample Quality Control Checklists 159Sample Statistical Analysis Plan 163Glossary 181References 195Index 197 Analysis of Clinical Trials Using SAS®: A Practical Guide, Second Edition bridges the gap between modern statistical methodology and real-world clinical trial applications.

Tutorial material and step-by-step instructions illustrated with examples from actual trials serve to define relevant statistical ...

Essential to effective validation is the programmer's understanding of the data with which they'll be working.

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.

All anesthesiologists eventually face the fear of a near miss, when a patient's life has been put at risk.

Learning from the experience is crucial to professionalism and the ongoing development of expertise. For Dylan Ice, a second-year law associate at a prestigious St. His clients want guardianship of their hospitalized daughter, Nicole Girard, because she is mentally unfit. Randomized clinical trials are the gold standard for establishing many clinical practice guidelines and are central to evidence based medicine. Includes all testable terms, concepts, persons, places, and events. Includes all testable terms, concepts, persons, places, and events. Includes all testable terms, concepts, persons, places, and events. Includes all testable terms, concepts, persons, places, and events.

I recommend this book to SAS programmers just entering the pharmaceutical industry, as well as to those that have years of experience in the industry. The authors' written style allows the reader to almost see and hear Carol and Brian sitting nearby in conversation. 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. Topics addressed include: Validation and pharmaceutical industry overviews Documentation and maintenance requirements discussions General techniques to facilitate validation Data importing and exporting Common data types Reporting and statistics Validating Clinical Trial Data Reporting with SAS is designed for SAS programmers who are new to the pharmaceutical industry as well as for those seeking a good foundation for validation in the SAS programming arena.

The SAS programming examples in the book are very clear and easy to follow. 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. Readers should have a working knowledge of Base SAS and a basic understanding of programming tasks in the pharmaceutical industry. Your book may arrive from Roseburg, OR, La Vergne, TN.

Validation is a critical component to programming clinical trial analysis.

Essential to effective validation is the programmer's understanding of the data with which they'll be working.

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.

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