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Full Description
The concepts of estimands, analyses (estimators), and sensitivity are interrelated. Therefore, great need exists for an integrated approach to these topics. This book acts as a practical guide to developing and implementing statistical analysis plans by explaining fundamental concepts using accessible language, providing technical details, real-world examples, and SAS and R code to implement analyses. The updated ICH guideline raises new analytic and cross-functional challenges for statisticians. Gaps between different communities have come to surface, such as between causal inference and clinical trialists, as well as among clinicians, statisticians, and regulators when it comes to communicating decision-making objectives, assumptions, and interpretations of evidence.
This book lays out a path toward bridging some of these gaps. It offers
A common language and unifying framework along with the technical details and practical guidance to help statisticians meet the challenges
A thorough treatment of intercurrent events (ICEs), i.e., postrandomization events that confound interpretation of outcomes and five strategies for ICEs in ICH E9 (R1)
Details on how estimands, integrated into a principled study development process, lay a foundation for coherent specification of trial design, conduct, and analysis needed to overcome the issues caused by ICEs:
A perspective on the role of the intention-to-treat principle
Examples and case studies from various areas
Example code in SAS and R
A connection with causal inference
Implications and methods for analysis of longitudinal trials with missing data
Together, the authors have offered the readers their ample expertise in clinical trial design and analysis, from an industrial and academic perspective.
Contents
Section I Setting the Stage 1. Introduction 2. Why Are Estimands Important? Section II Estimands 3. Estimands and How to Define Them 4. Strategies for Dealing with Intercurrent Events 5. Examples from Actual Clinical Trials in Choosing and Specifying Estimands 6. Causal Inference and Estimands 7. Putting the Principles into Practice Section III Estimators and Sensitivity 8. Overview of Estimators 9. Modeling Considerations 10. Overview of Analyses for Composite Intercurrent Event Strategies 11. Overview of Analyses for Hypothetical Intercurrent Event Strategies 12. Overview of Analyses for Principal Stratification Intercurrent Event Strategies 13. Overview of Analyses for While-on-Treatment Intercurrent Event Strategies 14. Overview of Analyses for Treatment Policy Intercurrent Event Strategies 15. Missing Data 16. Sensitivity Analyses Section IV Technical Details on Selected Analyses 17. Example Data 18. Direct Maximum Likelihood 19. Multiple Imputation 20. Inverse Probability Weighted Generalized Estimated Equations 21. Doubly Robust Methods 22. Reference-Based Imputation 23. Delta Adjustment 24. Overview of Principal Stratification Methods Section V Case Studies: Detailed Analytic Examples 25. Analytic Case Study of Depression Clinical Trials 26. Analytic Case Study Based on the ACTG 175 HIV Trial
