Titlebook: Statistics in Precision Health; Theory, Methods and Yichuan Zhao,Ding-Geng Chen Book 2024 The Editor(s) (if applicable) and The Author(s),

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Q-Learning Based Methods for Dynamic Treatment Regimesormalize the process of decision-making by mapping from patients’ observable information to a recommended treatment. Q-learning is a popular approach for estimating an optimal treatment regime. It is closely related to the regression-based analysis in statistics and belongs to the family of reinforc

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Personalized Medicine with Multiple Treatmentszed treatment rules that map patient pretreatment covariates to the space of available treatments to maximize the expected clinical outcome. Most existing research focused on scenarios where only two treatment options are applicable. Nevertheless, it is not uncommon to have more than two treatment o

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Statistical Reinforcement Learning and Dynamic Treatment Regimesdiscussion should be of interest to those who wish to go into the depth of statistical reinforcement learning. We start with introducing the Markov decision process. Then, several methods such as policy iteration, value iteration, temporal difference learning, and policy gradient are presented. Next

perimenopause

Integrative Learning to Combine Individualized Treatment Rules from Multiple Randomized Trialsprove treatment response of chronic disorders. However, several barriers, in particular, lack of generalizability or reproducibility of ITRs derived from a single study and lack of power to detect treatment modifiers as tailoring variables, pose serious challenges for implementing ITRs in clinical p

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Estimation and Inference for Individualized Treatment Rules Using Efficient Augmentation and Relaxatomes. One type of approaches to learn such rules estimates individualized treatment rules through a weighted classification framework. Particularly, in this chapter, we introduce doubly-robust learning methods called efficient augmentation and relaxation learning (EARL) and its extensions to high-di

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Subgroup Analysis Using Doubly Robust Semiparametric Procedures different between two subgroups of patients. One goal is then to identify if there exists such a subgroup in the patient population. Since the treatment assignment in the study population may not be covariate balanced, and the naive estimate of treatment effect such as a group mean may be biased. T

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A Selective Overview of Fusion Penalized Learning in Latent Subgroup Analysis for Precision MedicineBig data along with rapid growth in computational power are creating unprecedented opportunities in precision medicine that is at the forefront of medical research. Understanding the disease heterogeneity is essential to the development of precision medicine that aims to tailor treatments to subgrou