1st Edition
Estimands, Estimators and Sensitivity Analysis in Clinical Trials
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
Biography
Geert Molenberghs is Professor of Biostatistics (Hasselt University, KULeuven. He works on surrogate endpoints, longitudinal and incomplete data, was Editor for Applied Statistics, Biometrics, Biostatistics, Wiley Probability & Statistics, and Wiley StatsRef and is Executive Editor of Biometrics. He was President of the International Biometric Society, is Fellow of the American Statistical Association, and received the Guy Medal in Bronze from the Royal Statistical Society. He has held visiting positions at the Harvard School of Public Health.
Ilya Lipkovich is a Sr. Research Advisor at Eli Lilly and Company. He is a Fellow of the American Statistical Association and published on subgroup identification in clinical data, analysis with missing data, and causal inference. He is a frequent presenter at conferences, a co-developer of subgroup identification methods, and a co-author of the book "Analyzing Longitudinal Clinical Trial Data. A Practical Guide."
Bohdana Ratitch is a Principal Research Scientist at Eli Lilly and Company. Bohdana has contributed to research and practical applications of methodologies for causal inference and missing data in clinical trials through active participation in a pharma industry working group, numerous publications, presentations, and co-authoring the book "Clinical Trials with Missing Data: A Guide for Practitioners".
Craig Mallinckrodt holds the rank of Distinguished Biostatistician at Biogen in Cambridge MA. He has extensive experience in all phases of clinical research. His methodology research focuses on longitudinal and incomplete data. He is Fellow of the American Statistical Association, has led several industry working groups on missing and longitudinal data, and received the Royal Statistical Society’s award for outstanding contribution to the pharmaceutical industry.
"The purpose of this book, which is to promote an integrated understanding of key concepts throughout the drug development process through an example-based approach, is certainly achieved. It is the holistic approach to planning the analysis and the focus on practical implementation that distinguishes this text from others... Overall, I enjoyed reading this book, which is a holistic and complete work and will be useful for researchers in the medical statistics area."
- Taras Lukashiv, ISCB News, July 2020
