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With the emergence of precision medicine, estimating optimal individualized decision rules (IDRs) has attracted tremendous attention in many scientific areas. Most existing literature has focused on finding optimal IDRs that can maximize…

统计方法学 · 统计学 2022-06-28 Zhengling Qi , Jong-Shi Pang , Yufeng Liu

Recent exploration of optimal individualized decision rules (IDRs) for patients in precision medicine has attracted a lot of attention due to the heterogeneous responses of patients to different treatments. In the existing literature of…

最优化与控制 · 数学 2019-08-29 Zhengling Qi , Ying Cui , Yufeng Liu , Jong-Shi Pang

An individualized decision rule (IDR) is a decision function that assigns each individual a given treatment based on his/her observed characteristics. Most of the existing works in the literature consider settings with binary or finitely…

统计方法学 · 统计学 2023-01-31 Hengrui Cai , Chengchun Shi , Rui Song , Wenbin Lu

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

An individualized treatment rule (ITR) is a decision rule that aims to improve individual patients health outcomes by recommending optimal treatments according to patients specific information. In observational studies, collected data may…

统计方法学 · 统计学 2023-10-03 Zeyu Bian , Erica EM Moodie , Susan M Shortreed , Sylvie D Lambert , Sahir Bhatnagar

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

Personalized medicine has gained much popularity recently as a way of providing better healthcare by tailoring treatments to suit individuals. Our research, motivated by the UK INTERVAL blood donation trial, focuses on estimating the…

统计方法学 · 统计学 2023-02-24 Yuejia Xu , Angela M. Wood , David J. Roberts , Brian D. M. Tom

Individualized treatment rules (ITRs) are considered a promising recipe to deliver better policy interventions. One key ingredient in optimal ITR estimation problems is to estimate the average treatment effect conditional on a subject's…

统计方法学 · 统计学 2021-03-16 Hongming Pu , Bo Zhang

In precision medicine, one of the most important problems is estimating the optimal individualized treatment rules (ITR), which typically involves recommending treatment decisions based on fully observed individual characteristics of…

统计方法学 · 统计学 2025-10-15 Yue Zhang , Shanshan Luo , Zhi Geng , Yangbo He

Precision medicine is an emerging scientific topic for disease treatment and prevention that takes into account individual patient characteristics. It is an important direction for clinical research, and many statistical methods have been…

统计方法学 · 统计学 2017-02-17 Jingxiang Chen , Haoda Fu , Xuanyao He , Michael R. Kosorok , Yufeng Liu

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

Integrative analysis of multiple datasets for estimating optimal individualized treatment rules (ITRs) can enhance decision efficiency. A central challenge is posterior shift, wherein the conditional distribution of potential outcomes given…

机器学习 · 统计学 2026-03-09 Wenhai Cui , Wen Su , Xingqiu Zhao

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…

Individualized treatment effect lies at the heart of precision medicine. Interpretable individualized treatment rules (ITRs) are desirable for clinicians or policymakers due to their intuitive appeal and transparency. The gold-standard…

统计方法学 · 统计学 2021-08-20 Lili Wu , Shu Yang

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

Individualized treatment rule (ITR) recommends treatment on the basis of individual patient characteristics and the previous history of applied treatments and their outcomes. Despite the fact there are many ways to estimate ITR with binary…

统计方法学 · 统计学 2017-08-15 Pavel Shvechikov , Evgeniy Riabenko

We consider estimation of an optimal individualized treatment rule from observational and randomized studies when a high-dimensional vector of baseline variables is available. Our optimality criterion is with respect to delaying expected…

统计方法学 · 统计学 2017-11-09 Iván Díaz , Oleksandr Savenkov , Karla Ballman

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

Individualized treatment rules (ITRs) are deterministic decision rules that recommend treatments to individuals based on their characteristics. Though ubiquitous in medicine, ITRs are hardly ever evaluated in randomized controlled trials.…

统计方法学 · 统计学 2023-08-22 François Grolleau , Francois Petit , Raphaël Porcher

Recurrent events, characterized by the repeated occurrence of the same event in an individual, are a common type of data in medical research. Motivated by cancer recurrences, we aim to estimate the optimal individualized treatment regime…

统计方法学 · 统计学 2025-02-18 Zi-Shu Zhan , Jin-Lun Zhang , Chen Shi , Xiao-Han Xu , Chun-Quan Ou
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