Dynamic Treatment Regime: Statistical Methods for Precision Medicine, 1st Edition (Hardback) book cover

Dynamic Treatment Regime

Statistical Methods for Precision Medicine, 1st Edition

By Anastasios A. Tsiatis, Marie Davidian, Shannon T. Holloway, Eric B. Laber

Chapman and Hall/CRC

602 pages

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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.

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

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

About the Authors

Anastasios Tsiatis is Gertrude M. Cox Distinguished Professor Emeritus, Marie Davidian is J. Stuart Hunter Distinguished Professor, Shannon T. Holloway is Senior Research Scholar, and Eric B. Laber is 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.

About the Series

Chapman & Hall/CRC Monographs on Statistics and Applied Probability

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General
MED071000
MEDICAL / Pharmacology
MED107000
MEDICAL / Genetics