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Full Description
The statistical study and development of analytic methodology for individualization of treatments is no longer in its infancy. Many methods of study design, estimation, and inference exist, and the tools available to the analyst are ever growing. This handbook introduces the foundations of modern statistical approaches to precision medicine, bridging key ideas to active lines of current research in precision medicine.
The contributions in this handbook vary in their level of assumed statistical knowledge; all contributions are accessible to a wide readership of statisticians and computer scientists including graduate students and new researchers in the area. Many contributions, particularly those that are more comprehensive reviews, are suitable for epidemiologists and clinical researchers with some statistical training. The handbook is split into three sections: Study Design for Precision Medicine, Estimation of Optimal Treatment Strategies, and Precision Medicine in High Dimensions.
The first focuses on designed experiments, in many instances, building and extending on the notion of sequential multiple assignment randomized trials. Dose finding and simulation-based designs using agent-based modelling are also featured. The second section contains both introductory contributions and more advanced methods, suitable for estimating optimal adaptive treatment strategies from a variety of data sources including non-experimental (observational) studies. The final section turns to estimation in the many-covariate setting, providing approaches suitable to the challenges posed by electronic health records, wearable devices, or any other settings where the number of possible variables (whether confounders, tailoring variables, or other) is high. Together, these three sections bring together some of the foremost leaders in the field of precision medicine, offering new insights and ideas as this field moves towards its third decade.
Contents
Preface Part 1: Study Design For Precision Medicine 1. Adaptive Designs for Precision Medicine: Fundamental Statistical Considerations 2. Small Sample, Sequential, Multiple Assignment, Randomized Trial Design and Analysis 3. Sequential Multiple Assignment Randomized Trial with Adaptive Randomization (SMART-AR) for Mobile Health Devices 4. Bayesian Dose-Finding in Two Treatment Cycles based on Efficacy and Toxicity 5. Agent-Based Modeling in Medical Research - Example in Health Economics 6. Thompson Sampling for mHealth and Precision Health Applications Part 2: Estimation of Optimal Treatment Strategies 7. Constructing and Evaluating Optimal Treatment Sequences: An Introductory Guide for Bayesians 8. Measurement Error in Adaptive Treatment Strategies 9. Nonparametric Heterogeneous Treatment Effect Estimation in Repeated Cross Sectional Designs 10. Semiparametric Doubly Robust Targeted Double Machine Learning: A Review 11. Adversarial Monte Carlo Meta-Learning of Conditional Average Treatment Effects 12. Personalized Policy Learning 13. Bandit Algorithms for Precision Medicine Part 3: Precision Medicine in High Dimensions 14. Tailoring Variable Selection and Ranking for Optimal Treatment Decisions 15. Selecting Optimal Subgroups for Treatment Using Many Covariates 16. Statistical Learning Methods for Estimating Optimal Individualized Treatment Rules from Observational Data 17. Polygenic Risk Prediction for Precision Prevention 18. Post-Selection Inference for Individualized Treatment Rules with Nonparametric Confounding Control Bibliography
