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Personalized decision-making, aiming to derive optimal treatment regimes based on individual characteristics, has recently attracted increasing attention in many fields, such as medicine, social services, and economics. Current literature…

统计方法学 · 统计学 2023-02-28 Jianing Chu , Wenbin Lu , Shu Yang

Flexible estimation of the mean outcome under a treatment regimen (i.e., value function) is the key step toward personalized medicine. We define our target parameter as a conditional value function given a set of baseline covariates which…

统计理论 · 数学 2023-09-29 Ashkan Ertefaie , Luke Duttweiler , Brent A. Johnson , Mark J. van der Laan

To promote precision medicine, individualized treatment regimes (ITRs) are crucial for optimizing the expected clinical outcome based on patient-specific characteristics. However, existing ITR research has primarily focused on scenarios…

统计方法学 · 统计学 2024-02-20 Chang Wang , Lu Wang

Recent development in the data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and outcomes, policy makers best individualized treatment rule…

机器学习 · 统计学 2020-06-29 Weibin Mo , Zhengling Qi , Yufeng Liu

The field of precision medicine aims to tailor treatment based on patient-specific factors in a reproducible way. To this end, estimating an optimal individualized treatment regime (ITR) that recommends treatment decisions based on patient…

Data-driven individualized decision making has recently received increasing research interests. Most existing methods rely on the assumption of no unmeasured confounding, which unfortunately cannot be ensured in practice especially in…

统计方法学 · 统计学 2022-12-26 Zhengling Qi , Rui Miao , Xiaoke Zhang

With a large number of baseline covariates, we propose a new semi-parametric modeling strategy for heterogeneous treatment effect estimation and individualized treatment selection, which are two major goals in personalized medicine. We…

统计方法学 · 统计学 2021-08-12 Wenchuan Guo , Xiao-hua Zhou , Shujie Ma

We develop a mathematical framework to define an optimal individualized treatment rule (ITR) within the context of prioritized outcomes in a randomized controlled trial. Our optimality criterion is based on the framework of generalized…

统计方法学 · 统计学 2025-06-17 François Petit , Gérard Biau , Raphaël Porcher

Multistate process data are common in studies of chronic diseases such as cancer. These data are ideal for precision medicine purposes as they can be leveraged to improve more refined health outcomes, compared to standard survival outcomes,…

统计方法学 · 统计学 2022-11-28 Giorgos Bakoyannis

We consider optimal regimes for algorithm-assisted human decision-making. Such regimes are decision functions of measured pre-treatment variables and, by leveraging natural treatment values, enjoy a "superoptimality" property whereby they…

统计方法学 · 统计学 2024-02-23 Mats J. Stensrud , Julien Laurendeau , Aaron L. Sarvet

We study the design of multi-armed parallel group clinical trials to estimate personalized treatment rules that identify the best treatment for a given patient with given covariates. Assuming that the outcomes in each treatment arm are…

统计理论 · 数学 2022-07-13 David Azriel , Yosef Rinott , Martin Posch

Precision medicine involves developing individualized treatment regimes (ITRs) which allow for treatment decisions to be tailored to patient characteristics. Naturally, the identification of the optimal regime, that is, the rule which…

统计方法学 · 统计学 2025-09-30 Misha Dolmatov , Erica E. M. Moodie , David A. Stephens , Dipankar Bandyopadhyay

Dynamic treatment regimes are of growing interest across the clinical sciences as these regimes provide one way to operationalize and thus inform sequential personalized clinical decision making. A dynamic treatment regime is a sequence of…

统计方法学 · 统计学 2013-11-27 Eric B. Laber , Min Qian , Dan J. Lizotte , William E. Pelham , Susan A. Murphy

Modern precision medicine aims to utilize real-world data to provide the best treatment for an individual patient. An individualized treatment rule (ITR) maps each patient's characteristics to a recommended treatment scheme that maximizes…

应用统计 · 统计学 2025-01-07 Andong Wang , Kelly Wentzlof , Johnny Rajala , Miontranese Green , Yunshu Zhang , Shu Yang

Precision medicine is currently a topic of great interest in clinical and intervention science. One way to formalize precision medicine is through a treatment regime, which is a sequence of decision rules, one per stage of clinical…

统计方法学 · 统计学 2016-06-07 Yichi Zhang , Eric B. Laber , Anastasios Tsiatis , Marie Davidian

Personalized medicine has received increasing attention among statisticians, computer scientists, and clinical practitioners. A major component of personalized medicine is the estimation of individualized treatment rules (ITRs). Recently,…

统计方法学 · 统计学 2015-08-14 Xin Zhou , Nicole Mayer-Hamblett , Umer Khan , Michael R. Kosorok

An individualized treatment regime (ITR) is a decision rule that assigns treatments based on patients' characteristics. The value function of an ITR is the expected outcome in a counterfactual world had this ITR been implemented. Recently,…

统计方法学 · 统计学 2023-01-16 Pan Zhao , Julie Josse , Shu Yang

A treatment regime is a deterministic function that dictates personalized treatment based on patients' individual prognostic information. There is a fast-growing interest in finding optimal treatment regimes to maximize expected long-term…

统计理论 · 数学 2016-11-25 Runchao Jiang , Wenbin Lu , Rui Song , Marie Davidian

The sequential treatment decisions made by physicians to treat chronic diseases are formalized in the statistical literature as dynamic treatment regimes. To date, methods for dynamic treatment regimes have been developed under the…

统计方法学 · 统计学 2022-02-22 Janie Coulombe , Erica E. M. Moodie , Susan M. Shortreed , Christel Renoux

Learning individualized treatment rules (ITRs) is an important topic in precision medicine. Current literature mainly focuses on deriving ITRs from a single source population. We consider the observational data setting when the source…

机器学习 · 统计学 2023-07-04 Rui Chen , Jared D. Huling , Guanhua Chen , Menggang Yu
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