The concepts of estimands, analyses (estimators) and sensitivity are interrelated. Therefore, great need exists for an integrated approach to these topics. This book acts 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.
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.