Dynamic treatment regimes : statistical methods for precision medicine /: statistical methods for precision medicine. (2019)

• Record Type: Book
• Further Information: Note: Anastasios A. Tsiatis, Marie Davidian, Shannon T. Holloway, and Eric B. Laber.
• Authors: Tsiatis, Anastasios A (Anastasios Athanasios)
• 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
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2019
• Extent: 1 online resource
• Subjects: 610.21; Medical statistics; Medical records -- Data processing
• Languages: English
• ISBNs: 9780429532221; 9781498769785; 9780429546921; 9780429192692
• Related ISBNs: 9781498769778
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.