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Dynamic Treatment Regimes
Statistical Methods for Precision Medicine




ISBN 9781498769778
Published December 10, 2019 by Chapman and Hall/CRC
602 Pages

 
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Book Description

Dynamic Treatment Regimes: Statistical Methods for Precision Medicine provides a comprehensive introduction to statistical methodology for the evaluation and discovery of dynamic treatment regimes from data. Researchers and graduate students in statistics, data science, and related quantitative disciplines with a background in probability and statistical inference and popular statistical modeling techniques will be prepared for further study of this rapidly evolving field.

A dynamic treatment regime is a set of sequential decision rules, each corresponding to a key decision point in a disease or disorder process, where each rule takes as input patient information and returns the treatment option he or she should receive. Thus, a treatment regime formalizes how a clinician synthesizes patient information and selects treatments in practice. Treatment regimes are of obvious relevance to precision medicine, which involves tailoring treatment selection to patient characteristics in an evidence-based way. Of critical importance to precision medicine is estimation of an optimal treatment regime, one that, if used to select treatments for the patient population, would lead to the most beneficial outcome on average. Key methods for estimation of an optimal treatment regime from data are motivated and described in detail. A dedicated companion website presents full accounts of application of the methods using a comprehensive R package developed by the authors.

The authors’ website www.dtr-book.com includes updates, corrections, new papers, and links to useful websites.

Table of Contents

Preface

1. Introduction
   What is a Dynamic Treatment Regime?
   Motivating Examples
   Treatment of Acute Leukemias
   Interventions for Children with ADHD
   Treatment of HIV Infection
   The Meaning of \Dynamic"
   Basic Framework 8
   Definition of a Dynamic Treatment Regime
   Data for Dynamic Treatment Regimes
   Outline of this Book

2. Preliminaries
   Introduction
   Point Exposure Studies
   Potential Outcomes and Causal Inference
   Potential Outcomes
   Randomized Studies
   Observational Studies
   Estimation of Causal E ects via Outcome Regression
   Review of M-estimation
   Estimation of Causal E ects via the Propensity Score
   The Propensity Score
   Propensity Score Stratification
   Inverse Probability Weighting
   Doubly Robust Estimation of Causal E ects
   Application

3. Single Decision Treatment Regimes: Fundamentals
   Introduction
   Treatment Regimes for a Single Decision Point
   Class of All Possible Treatment Regimes
   Potential Outcomes Framework
   Value of a Treatment Regime
   Estimation of the Value of a Fixed Regime 
   Outcome Regression Estimator
   Inverse Probability Weighted Estimator
   Augmented Inverse Probability Weighted Estimator
   Characterization of an Optimal Regime
   Estimation of an Optimal Regime
   Regression-based Estimation
   Estimation via A-learning
   Value Search Estimation
   Implementation and Practical Performance
   More Than Two Treatment Options
   Application

4. Single Decision Treatment Regimes: Additional Methods
   Introduction
   Optimal Regimes from a Classification Perspective
   Generic Classification Problem
   Classification Analogy
   Outcome Weighted Learning
   Interpretable Treatment Regimes Via Decision Lists
   Additional Approaches
   Application

5. Multiple Decision Treatment Regimes: Overview
   Introduction
   Multiple Decision Treatment Regimes
   Statistical Framework
   Potential Outcomes for K Decisions
   Data
   Identifiability Assumptions
   The g-Computation Algorithm
   Estimation of the Value of a Fixed Regime
   Estimation via g-Computation
   Inverse Probability Weighted Estimator
   Characterization of an Optimal Regime
   Estimation of an Optimal Regime
   Q-learning
   Value Search Estimation
   Backward Iterative Implementation of Value Search Estimation
   Implementation and Practical Performance
   Application

6. Multiple Decision Treatment Regimes: Formal Framework
   Introduction
   Statistical Framework
   Potential Outcomes for K Decisions
   Feasible Sets and Classes of Treatment Regimes
   Potential Outcomes for a Fixed K-Decision Regime
   Identifiability Assumptions
   The g-Computation Algorithm
   Estimation of the Value a Fixed Regime
   Estimation via g-Computation
   Regression-Based Estimation
   Inverse Probability Weighted Estimator
   Augmented Inverse Probability Weighted Estimator
   Estimation via Marginal Structural Models
   Application

7. Optimal Multiple Decision Treatment Regimes
   Introduction
   Characterization of an Optimal Regime
   Specific Regimes
   Characterization in Terms of Potential Outcomes
   Justification
   Characterization in Terms of Observed Data
   Optimal \Midstream" Regimes
   Estimation of an Optimal Regime
   Q-learning
   A-learning
   Value Search Estimation
   Backward Iterative Estimation
   Classification Perspective
   Interpretable Regimes via Decision Lists
   Estimation via Marginal Structural Models
   Additional Approaches
   Implementation and Practical Performance
   Application

8. Regimes Based on Time-to-Event Outcomes
   Introduction
   Single Decision Treatment Regimes
   Statistical Framework
   Outcome Regression Estimators
   Inverse Probability of Censoring Regression Estimators
   Inverse Probability Weighted and Value Search Estimators
   Discussion
   Multiple Decision Treatment Regimes
   Multiple Decision Regimes
   Statistical Framework
   Estimation of the Value of a Fixed Regime
   Characterization of an Optimal Regime
   Estimation of an Optimal Regime
   Discussion
   Application
   Technical Details

9. Sequential Multiple Assignment Randomized Trials
   Introduction
   Design Considerations
   Basic SMART Framework, K = 2
   Critical Decision Points
   Feasible Treatment Options
   Interim Outcomes, Randomization, and Stratification
   Other Candidate Designs
   Power and Sample Size for Simple Comparisons
   Comparing Response Rates
   Comparing Fixed Regimes
   Power and Sample Size for More Complex Comparisons
   Marginalizing Versus Maximizing
   Marginalizing Over the Second Stage
   Marginalizing With Respect to Standard of Care
   Maximizing Over the Second Stage
   Power and Sample Size for Optimal Treatment Regimes
   Normality-based Sample Size Procedure
   Projection-based Sample Size Procedure
   Extensions and Further Reading

10. Statistical Inference
    Introduction
    Nonsmoothness and Statistical Inference
    Inference for Single Decision Regimes
    Inference on Model Parameters
    Inference on the Value
    Inference for Multiple Decision Regimes
    Q-learning
    Value Search Estimation with Convex Surrogates
    g-Computation
    Discussion

11. Additional Topics

 

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Author(s)

Biography

Anastasios Tsiatis is Gertrude M. Cox Distinguished Professor Emeritus, Marie Davidian is J. Stuart Hunter Distinguished Professor, Shannon Holloway is Senior Research Scholar, and Eric Laber is Goodnight Distinguished Professor, all in the Department of Statistics at North Carolina State University. They have published extensively and are internationally-recognized authorities on methodology for dynamic treatment regimes.

Reviews

"Biostatisticians, those that are professional as well as masters level and PhD level, will find this book useful. It is written by well-known experts who have incredible track records in this field, both methodologically and in designing and implementing/analyzing SMARTs and observational studies to uncover optimal dynamic treatment regimes. The text is rigorous in its statistical definitions and theorems. It is a comprehensive text on the area of dynamic treatment regimes and SMART design. Both those familiar with this area and those new to the area will learn something. They offer some interesting uses of the SMART design (e.g., dose finding and extending beyond 2 stages), that you cannot find in current manuscripts."
~Kelley Kidwell, University of Michigan

"The book will serve as an excellent reference and textbook. I expect I will use the book in my own class, once it is available. Besides being a comprehensive treatment of dynamic treatment regimes, the revision/re-introduction to causal inference, potential outcomes, M-estimators, propensity scores, and related issues is extremely useful."
~Daniel Lizotte, The University of Western Ontario

"Statisticians/biostatisticians directly involved in planning SMARTs would likely find this material useful, as they would have to adapt or extend these methods to particular trials being planned. Also, academic statisticians aiming to get into this field of methodological research would likely find the material as a useful summary of the already extensive literature; however, as the field is fast-moving, this material only serves as a starting point. The authors are not providing a cookbook-style guide to planning a variety of different kind of SMARTs, they provide examples, and enough theoretical background rigorously presented to get started in the area."
~Olli Saarela, University of Toronto

"(Chapters 2-4) are very nice indeed: well written, well structured, informative and interesting. Congratulations to the authors… I went to the website, which is beautiful. The authors have put lots of effort into this.
~Robin Henderson, Newcastle University