Dynamic Treatment Regimes: Statistical Methods for Precision Medicine [Paperback]

1. Introduction. 2. Preliminaries. 3. Single Decision Treatment Regimes: Fundamentals. 4. Single Decision Treatment Regimes: Additional Methods. 5. Multiple Decision Treatment Regimes: Overview. 6. Multiple Decision Treatment Regimes: Formal Framework. 7. Optimal Multiple Decision Treatment Regimes. 8. Regimes Based on Time-to-Event Outcomes. 9. Sequential Multiple Assignment Randomized Trials. 10. Statistical Inference. 11.Additional TopicsBibliography.

Precision medicine seeks to use data to construct principled, i.e., evidence-based, treatment strategies that dictate where, when, and to whom treatment should be applied. This book provides an accessible yet comprehensive introduction to statistical methodology for dynamic treatment regimes.

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 lÃ7