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Analysis of Clinical Trials Using SAS

Author Geert Molenberghs , Walter W. Offen , Christy Chuang-Stein , Alex Dmitrienko
Release Date: 2005/02/01
ISBN: 9781590475041
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Book Description

This comprehensive guide bridges the gap between modern statistical methodology and real-world clinical trial applications. Step-by-step instructions illustrated with examples from actual trials and case studies serve to define a statistical method and its relevance in a clinical trials setting and to illustrate how to implement the method rapidly and efficiently using the power of SAS software. Topics reflect the International Conference on Harmonization (ICH) guidelines for the pharmaceutical industry and address important statistical problems encountered in clinical trials, including analysis of stratified data, incomplete data, multiple inferences, issues arising in safety and efficacy monitoring, and reference intervals for extreme safety and diagnostic measurements. Clinical statisticians, research scientists, and graduate students in biostatistics will greatly benefit from the decades of clinical research experience compiled in this book. Numerous ready-to-use SAS macros and example code are included.

Table of Contents

  1. 1 Analysis of Stratified Data
    1. 1.1 Introduction
    2. 1.2 Continuous Endpoints
    3. 1.3 Categorical Endpoints
    4. 1.4 Time-to-Event Endpoints
    5. 1.5 Tests for Qualitative Interactions
  2. 2 Multiple Comparisons and Multiple
    1. 2.1 Introduction
    2. 2.2 Single-Step Tests
    3. 2.3 Closed Testing Methods
    4. 2.4 Fixed-Sequence Testing Methods
    5. 2.5 Resampling-Based Testing Methods
    6. 2.6 Testing Procedures for Multiple Endpoints
    7. 2.7 Gatekeeping Strategies
  3. 3 Analysis of Safety and Diagnostic Data
    1. 3.1 Introduction
    2. 3.2 Reference Intervals for Safety and Diagnostic Measures
    3. 3.3 Analysis of Shift Tables
  4. 4 Interim Data Monitoring
    1. 4.1 Introduction
    2. 4.2 Repeated Significance Tests
    3. 4.3 Stochastic Curtailment Tests
  5. 5 Analysis of Incomplete Data
    1. 5.1 Introduction
    2. 5.2 Case Studies
    3. 5.3 Data Setting and Modeling Framework
    4. 5.4 Analysis of Complete Growth Data
    5. 5.5 Simple Methods and MCAR
    6. 5.6 Available Case Methods
    7. 5.7 Likelihood-Based Ignorable Analyses
    8. 5.8 Multiple Imputation
    9. 5.9 The EM Algorithm
    10. 5.10 Categorical Data
    11. 5.11 MNAR and Sensitivity Analysis
    12. 5.12 Summary
  6. Appendix
  7. References
  8. Index
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