Full Description
Does This Treatment Cause That Outcome?: The Science of Estimating a Treatment Effect and Why It Matters is an engaging and insightful exploration of cause-and-effect relationships in clinical research. It begins with the foundational principles of causal inference, traces the historical evolution of randomized controlled trials, and provides a clear, comprehensive explanation of the essential elements of ICH E9(R1). The central themes of ICH E9(R1) - defining the clinical question of primary importance and establishing the estimand to answer that question - are seamlessly integrated throughout the narrative.
A standout feature is the introduction of the Tripartite Estimand Approach, a groundbreaking framework derived from patient and physician perspectives. This approach addresses the critical questions and answers needed for informed prescribing decisions. The book also outlines a stepwise, logical process for implementing the estimand framework, offering practical guidance for clinicians, statisticians, and other professionals involved in clinical drug development. By simplifying complex concepts, this book aims to make the estimand framework more accessible and actionable across disciplines. While aligned with the principles of ICH E9(R1), the book goes beyond the established guidelines, presenting bold new ideas and perspectives that enhance understanding of estimands.
Features:
The importance of randomization and complete data for cause-and-effect inference
A novel definition of incomplete data
A focus on the two fundamental clinical treatment effect questions underlying an estimand
A comprehensive definition of treatment attributes, including a new attribute describing the treatment effect
A simplified approach to intercurrent events (IEs)
A systematic process for defining an estimand, building on estimand attributes and strategies for handling IEs
Numerous examples spanning diverse disease states and study designs
And much more!
Written in a conversational style with minimal mathematical notation, this book is designed to be accessible to clinicians and non-statistical professionals, making it an invaluable resource for anyone involved in clinical drug development. Whether you are a seasoned statistician or new to the field, the book provides the tools and insights needed to navigate the estimand framework with confidence and clarity.
Contents
Part 1: Some History begins with Chapter 1: The Goal of Science, which includes subtopics 1.1 Cause-and-Effect, 1.2 The Light Switch, 1.3 Clinical Trials as Scientific Experiments, 1.4 The Treatment Effect(s), 1.5 The Crucial Equivalence, and 1.6 Summary So Far. Chapter 2: How Did We Get Here? covers 2.1 Some History of Clinical Research, 2.2 Statistics and Clinical Research, 2.3 The University Group Diabetes Program (UGDP), 2.4 More Statistical Rigor, 2.5 Intent to Treat as the Default Standard, 2.6 The National Research Council Report on Missing Data, 2.7 The Estimand, 2.8 Dapagliflozin and the FDA Advisory Committee Meeting, 2.9 Estimands - Broadening the Perspective, 2.10 The Stuff of Life, and 2.11 Summary So Far. Chapter 3: ICH E9(R1) Addendum on Estimands includes 3.1 What is the Question? An Example, 3.2 Another Example: Alzheimer's Disease, and 3.3 Causal Inference. Part 2: ICH E9(R1) Explained begins with Chapter 4: The Four Attributes, which includes 4.1 Background, 4.2 Intercurrents Events, 4.3 Attribute 1: What Is the Treatment?, 4.4 Examples of Treatment Descriptions in the Medical Literature, 4.5 The Estimand-Defined Study Treatment, 4.6 A (Very Big) Missed Piece, 4.7 What Is the Treatment Effect Questions?, 4.8 Attribute 2: What is the Population?, 4.9 Attribute 3: What is the Variable?, 4.10 Attribute 4: What Is the Population-level Summary Measure, and 4.11 Summary. Chapter 5: The Five Strategies for Intercurrent Events includes 5.1 A Thought Experiment, 5.2 Some Early Commentary on the ITT Approach, 5.3 Summary So Far, 5.4 The Five Strategies, 5.5 Incomplete Data and Intercurrent Events, 5.6 Somethin's Gotta Give, 5.7 Strategic Thinking, and 5.8 Summary. Chapter 6: The Tripartite Estimand Approach includes 6.1 Mixture Distribution, 6.2 An Illustrative Example, 6.3 The Tripartite Estimand Approach (TEA), 6.4 Visualization, and 6.5 Summary. Part 3: Weaving the Golden Thread begins with Chapter 7: Implementation of the Estimand Framework, which includes 7.1 The Story So Far, 7.2 The Succinct Estimand Framework, 7.3 An Illustrative Example Based in Reality, and 7.4 Summary. Chapter 8: Examples of the Golden Thread includes 8.1 Preamble, 8.2 Example 1: Covid-19 - A Vaccine Strategy, 8.3 Example 2: Graft versus Host Disease (GVHD), 8.4 Example 3: Major Depressive Disorder, 8.5 Example 4: Hepatocellular Carcinoma, 8.6 Example 5: Age-Related Macular Degeneration, and 8.7 Example 6: Chimeric Antigen Receptor T-cell Therapy. Part 4: Epistemology - What is the Truth? begins with Chapter 9: What Do We Mean by the Mean?, which includes 9.1 Suppose We Knew the Truth, 9.2 In Search of the Truth!, 9.3 What Do We Mean by the Null Hypothesis?, and 9.4 Summary. Chapter 10: Potential Explanations of the Estimand includes 10.1 Background, 10.2 Potential Outcomes, 10.3 Principal Stratification, 10.4 The Connection to ICH E9(R1), 10.5 The Tripartite Estimand Approach, and 10.6 Summary. The Epilogue includes Chapter 11: Epilogue, with subtopics 11.1 Principles, 11.2 Process, 11.3 Practicalities, and 11.4 Progress, followed by References.
