Analysis of Clinical Trials Using SAS, 2nd Edition

Book Description

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 approaches, describe their clinical trial applications, and implement the approaches rapidly and efficiently using the power of SAS. Topics reflect the International Conference on Harmonization (ICH) guidelines for the pharmaceutical industry and address important statistical problems encountered in clinical trials. Commonly used methods are covered, including dose-escalation and dose-finding methods that are applied in Phase I and Phase II clinical trials, as well as important trial designs and analysis strategies that are employed in Phase II and Phase III clinical trials, such as multiplicity adjustment, data monitoring, and methods for handling incomplete data. This book also features recommendations from clinical trial experts and a discussion of relevant regulatory guidelines. This new edition includes more examples and case studies, new approaches for addressing statistical problems, and the following new technological updates: SAS procedures used in group sequential trials (PROC SEQDESIGN and PROC SEQTEST) SAS procedures used in repeated measures analysis (PROC GLIMMIX and PROC GEE) macros for implementing a broad range of randomization-based methods in clinical trials, performing complex multiplicity adjustments, and investigating the design and analysis of early phase trials (Phase I dose-escalation trials and Phase II dose-finding trials) Clinical statisticians, research scientists, and graduate students in biostatistics will greatly benefit from the decades of clinical research experience and the ready-to-use SAS macros compiled in this book.

Table of Contents

  1. Preface
  2. About This Book
  3. About These Authors
  4. 1 Model-based and Randomization-based Methods By Alex Dmitrienko and Gary G. Koch
    1. 1.1 Introduction
    2. 1.2 Analysis of continuous endpoints
    3. 1.3 Analysis of categorical endpoints
    4. 1.4 Analysis of time-to-event endpoints
    5. 1.5 Qualitative interaction tests
    6. References
  5. 2 Advanced Randomization-based Methods By Richard C. Zink, Gary G. Koch, Yunro Chung and Laura Elizabeth Wiener
    1. 2.1 Introduction
    2. 2.2 Case studies
    3. 2.3 %NParCov4 macro
    4. 2.4 Analysis of ordinal endpoints using a linear model
    5. 2.5 Analysis of binary endpoints
    6. 2.6 Analysis of ordinal endpoints using a proportional odds model
    7. 2.7 Analysis of continuous endpoints using the log-ratio of two means
    8. 2.8 Analysis of count endpoints using log-incidence density ratios
    9. 2.9 Analysis of time-to-event endpoints
    10. 2.10 Summary
  6. 3 Dose-Escalation Methods By Guochen Song, Zoe Zhang, Nolan Wages, Anastasia Ivanova, Olga Marchenko and Alex Dmitrienko
    1. 3.1 Introduction
    2. 3.2 Rule-based methods
    3. 3.3 Continual reassessment method
    4. 3.4 Partial order continual reassessment method
    5. 3.5 Summary
    6. References
  7. 4 Dose-finding Methods By Srinand Nandakumar, Alex Dmitrienko and Ilya Lipkovich
    1. 4.1 Introduction
    2. 4.2 Case studies
    3. 4.3 Dose-response assessment and dose-finding methods
    4. 4.4 Dose finding in Case study 1
    5. 4.5 Dose finding in Case study 2
    6. References
  8. 5 Multiplicity Adjustment Methods By Thomas Brechenmacher and Alex Dmitrienko
    1. 5.1 Introduction
    2. 5.2 Single-step procedures
    3. 5.3 Procedures with a data-driven hypothesis ordering
    4. 5.4 Procedures with a prespecified hypothesis ordering
    5. 5.5 Parametric procedures
    6. 5.6 Gatekeeping procedures
    7. References
    8. Appendix
  9. 6 Interim Data Monitoring By Alex Dmitrienko and Yang Yuan
    1. 6.1 Introduction
    2. 6.2 Repeated significance tests
    3. 6.3 Stochastic curtailment tests
    4. References
  10. 7 Analysis of Incomplete Data By Geert Molenberghs and Michael G. Kenward
    1. 7.1 Introduction
    2. 7.2 Case Study
    3. 7.3 Data Setting and Methodology
    4. 7.4 Simple Methods and MCAR
    5. 7.5 Ignorable Likelihood (Direct Likelihood)
    6. 7.6 Direct Bayesian Analysis (Ignorable Bayesian Analysis)
    7. 7.7 Weighted Generalized Estimating Equations
    8. 7.8 Multiple Imputation
    9. 7.9 An Overview of Sensitivity Analysis
    10. 7.10 Sensitivity Analysis Using Local Influence
    11. 7.11 Sensitivity Analysis Based on Multiple Imputation and Pattern-Mixture Models
    12. 7.12 Concluding Remarks
    13. References
  11. Index

Product Information

  • Title: Analysis of Clinical Trials Using SAS, 2nd Edition
  • Author(s): Alex Dmitrienko, Gary G. Koch
  • Release date: July 2017
  • Publisher(s): SAS Institute
  • ISBN: 9781635261448