Estimands, Estimators and Sensitivity Analysis in Clinical Trials: 1st Edition (Hardback) book cover

Estimands, Estimators and Sensitivity Analysis in Clinical Trials

1st Edition

By Craig Mallinckrodt, Geert Molenberghs, Ilya Lipkovich, Bohdana Ratitch

Chapman and Hall/CRC

312 pages | 20 B/W Illus.

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Hardback: 9781138592506
pub: 2019-12-23
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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; providing real world examples and providing 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 between clinicians, statisticians, and regulators when it comes to communicating decision-making objectives, assumptions, and interpretations of evidence.

This book lays out a path towards 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., post-randomization 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.

Jointly, the authors have offered the readers their ample expertise in clinical trial design and analysis, from an industrial as well as academic perspective.

Table of Contents

Part 1: Setting the Stage. 1. Introduction. 2. A Real World View of Why Estimands are Important. Part 2: Estimands. 3. Estimands: Overview and Concepts. 4. Choosing Estimands in Clinical Trials. 5. Dealing with Inter-Current events. 6. The Estimand Decision Tree. 7. Case Studies: Real World Examples in Choosing Estimands. Part 3: Estimators. 8. Analysis Framework for Dealing with Inter-Current Events. 9. Estimators for Composite Approaches. 10. Introduction to Hypothetical Approaches for De-Jure Estimands. 11. Likelihood Based Methods. 12. Multiple Imputation. 13. Introduction to Hypothetical Approaches for De Facto Estimors. 14. Model-based Approaches to MNAR. 15. Multiple Imputation Based Approaches for Controlled Imputation. 16. Likelihood Based Approaches for Controlled Imputation. 17. Approaches for Categorical Repeated Measures. 18. Approaches to Time-to-Event Endpoints. 19. Principal Stratification Approaches. Part 4: Sensitivity Analysis. 20. Basic Ideas and Concepts. 21. Sensitivity for Composite Approaches. 22. Sensitivity for Hypothetical Approaches. 23. Sensitivity for Time to Event Endpoints. 24. Sensitivity for Principal Stratification Approaches.

About the Authors

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.

About the Series

Chapman & Hall/CRC Biostatistics Series

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General
MEDICAL / Pharmacology
MEDICAL / Biostatistics