中文
相关论文

相关论文: Dimension-reduced outcome-weighted learning for es…

200 篇论文

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

An individualized treatment rule (ITR) tailors treatments to a patient's specific characteristics. However, randomized controlled trials (RCTs) are often underpowered to detect the treatment effect heterogeneity needed for reliable ITR…

统计方法学 · 统计学 2026-04-14 Yuan Bian , Donglin Zeng , Hyun-Joon Yang , Leanne M. Williams , Yuanjia Wang

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

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

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

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

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

Recent advances in dynamic treatment regimes (DTRs) facilitate the search for optimal treatments, which are tailored to individuals' specific needs and able to maximize their expected clinical benefits. However, existing algorithms relying…

机器学习 · 统计学 2024-10-18 Hanwen Ye , Wenzhuo Zhou , Ruoqing Zhu , Annie Qu

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

Traditional instrumental variable (IV) estimators face a fundamental constraint: they can only accommodate as many endogenous treatment variables as available instruments. This limitation becomes particularly challenging in settings where…

机器学习 · 计算机科学 2025-06-25 Shiangyi Lin , Hui Lan , Vasilis Syrgkanis

Synthesizing information from multiple data sources is crucial for constructing accurate individualized treatment rules (ITRs). However, privacy concerns often present significant barriers to the integrative analysis of such multi-source…

统计方法学 · 统计学 2025-11-11 Nan Qiao , Wangcheng Li , Jingxiao Zhang , Canyi Chen

The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers potential to improve patient outcomes by leveraging this heterogeneity. Individualized treatment rules (ITRs)…

Individualized treatment rules (ITRs) have been widely applied in many fields such as precision medicine and personalized marketing. Beyond the extensive studies on ITR for binary or multiple treatments, there is considerable interest in…

统计方法学 · 统计学 2024-03-08 Qi Xu , Xiaoke Cao , Geping Chen , Hanqi Zeng , Haoda Fu , Annie Qu

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

Medical treatments tailored to a patient's baseline characteristics hold the potential of improving patient outcomes while reducing negative side effects. Learning individualized treatment rules (ITRs) often requires aggregation of multiple…

机器学习 · 统计学 2022-12-15 Jay Jojo Cheng , Jared D. Huling , Guanhua Chen

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

An optimal individualized treatment rule (ITR) is a function that takes a patient's characteristics, such as demographics, biomarkers, and treatment history, and outputs a treatment that is expected to give the best outcome for that…

统计方法学 · 统计学 2026-02-05 Augustine Wigle , Erica E. M. Moodie

Individualized treatment rules/recommendations (ITRs) aim to improve patient outcomes by tailoring treatments to the characteristics of each individual. However, when there are many treatment groups, existing methods face significant…

统计方法学 · 统计学 2025-05-27 Ke Zhu , Jianing Chu , Ilya Lipkovich , Wenyu Ye , Shu Yang

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
‹ 上一页 1 2 3 10 下一页 ›