Dynamic Treatment Regimes Statistical Methods for Precision Medicine

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.

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 Book2. 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 Application3. 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 Application5. 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 Application7. 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 Application8. 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 Details9. 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 Reading10. 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 Discussion11. Additional Topics

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.