| Description |
1 online resource : illustrations (black and white) |
| Series |
Chapman and Hall/CRC Biostatistics Ser |
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Chapman & Hall/CRC biostatistics series.
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| Contents |
Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- List of Figures -- List of Tables -- List of Code Fragments -- Preface -- Acknowledgements -- Authors -- Section I: Setting the Stage -- 1: Introduction -- 1.1 Understanding the Problem -- 1.2 History -- 1.3 Summary -- 2: Why Are Estimands Important? -- 2.1 Motivating Example -- 2.2 How Estimands Help -- 2.3 Considerations for Differing Stakeholders -- 2.4 Considerations for Differing Clinical Situations -- 2.5 Summary -- Section II: Estimands -- 3: Estimands and How to Define Them -- 3.1 Introduction |
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3.2 Study Development Process Chart -- 3.3 Process for Defining Estimands -- 3.3.1 Introduction -- 3.3.2 Identifying Intercurrent Events -- 3.3.3 Defining the Treatment Regimen of Interest -- 3.3.4 Overview of Strategies for Handling Intercurrent Events -- 3.4 Defining the Estimand -- 3.5 Special Considerations in Defining Estimands -- 3.5.1 Estimands for Safety Outcomes -- 3.5.2 Estimands for Early-Phase Trials -- 3.5.3 Estimands for Scenarios When Treatment and Outcomes Do Not Occur Concurrently -- 3.5.4 Estimands for Quality-of-Life Evaluation in Trials with Many Deaths |
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3.5.5 Estimands for Assessing Non-inferiority -- 3.6 Trial Design and Conduct Considerations -- 3.6.1 Introduction -- 3.6.2 Data Collection and Trial Conduct Considerations -- 3.6.3 Trial Design Considerations -- 3.7 A Note on Missing Data -- 3.8 A Note on the Intention to Treat Principle -- 3.9 Summary -- 4: Strategies for Dealing with Intercurrent Events -- 4.1 Introduction -- 4.2 Treatment Policy Strategy -- 4.3 Composite Strategy -- 4.4 Hypothetical Strategy -- 4.5 Principal Stratification Strategy -- 4.6 While-on-Treatment Strategy |
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4.7 Assumptions Behind the Strategies for Dealing with Intercurrent Events -- 4.7.1 General Assumptions -- 4.7.2 Treatment Policy Strategy Assumptions -- 4.7.3 Composite Strategy Assumptions -- 4.7.4 Hypothetical Strategy Assumptions -- 4.7.5 Principal Stratification Assumptions -- 4.7.6 While-on-Treatment Strategy Assumptions -- 4.8 Risk-Benefit Implications -- 4.9 Summary -- 5: Examples from Actual Clinical Trials in Choosing and Specifying Estimands -- 5.1 Introduction -- 5.2 Example 1: A Proof of Concept Trial in Major Depressive Disorder -- 5.2.1 Background -- 5.2.2 Trial Description |
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5.2.3 Primary Estimand -- 5.3 Example 2: A Confirmatory Trial in Asthma -- 5.3.1 Background -- 5.3.2 Trial Description -- 5.3.3 Primary Estimand -- 5.3.4 Supportive Estimand -- 5.4 Example 3: A Confirmatory Trial in Rheumatoid Arthritis -- 5.4.1 Background -- 5.4.2 Trial Description -- 5.4.3 Estimand for RA Study Design 1 -- 5.4.4 Estimand 2 for RA Study Design 2 -- 5.5 Summary -- 6: Causal Inference and Estimands -- 6.1 Introduction -- 6.2 Causal Framework for Estimands in Clinical Trials -- 6.2.1 Defining Potential Outcomes -- 6.2.2 Counterfactual Outcomes and Potential Outcomes |
| Summary |
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 |
| Subject |
Clinical trials -- Statistical methods
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Mathematical models.
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Biometry.
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Clinical Trials as Topic -- statistics & numerical data
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Models, Statistical
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Models, Theoretical
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Biometry
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mathematical models.
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biometrics.
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MEDICAL -- Biostatistics.
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MEDICAL -- Pharmacology.
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Mathematical models
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Biometry
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Clinical trials -- Statistical methods
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| Form |
Electronic book
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| Author |
Molenberghs, Geert, author
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Lipkovich, Ilya, author
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Ratitch, Bohdana, author
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| ISBN |
9780429950063 |
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0429950063 |
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9780429950056 |
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0429950055 |
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9780429950063 |
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9780429950049 |
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0429950047 |
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9780429488825 |
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0429488823 |
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