ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2017
On Estimation of Optimal Treatment Regimes For Maximizing
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sequential multiple assignment randomized trial studies should report all key components: a systematic review.Journal of clinical epidemiology · 2022Pooled it
- Trial
- Optimizing Treatment Decision Estimation for Right-Censored Survival Data Through Parameter Transfer Learning.Statistics in medicine · 2026Article
- Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data.Journal of machine learning research : JMLR · 2025Article
- Estimation of optimal treatment regimes with electronic medical record data using the residual life value estimator.Biostatistics (Oxford, England) · 2024Article
- Estimating individualized treatment rules by optimizing the adjusted probability of a longer survival.Statistical methods in medical research · 2024Article
- Selection of the optimal personalized treatment from multiple treatments with right-censored multivariate outcome measures.Journal of applied statistics · 2024Article
- A general framework for subgroup detection via one-step value difference estimation.Biometrics · 2023Article
- Optimizing Treatment for Human Immunodeficiency Virus to Improve Clinical Outcomes Using Precision Medicine.American journal of epidemiology · 2023Article
- Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring.Biometrika · 2023Article
- Accountable survival contrast-learning for optimal dynamic treatment regimes.Scientific reports · 2023Article
- The Sequential Multiple Assignment Randomized Trial for Controlling Infectious Diseases: A Review of Recent Developments.American journal of public health · 2023Review
- Targeted Search for Individualized Clinical Decision Rules to Optimize Clinical Outcomes.Statistics in biosciences · 2022Article
- Semiparametric single-index models for optimal treatment regimens with censored outcomes.Lifetime data analysis · 2022Article
- Multithreshold change plane model: Estimation theory and applications in subgroup identification.Statistics in medicine · 2021Article
- On restricted optimal treatment regime estimation for competing risks data.Biostatistics (Oxford, England) · 2021Article
- Identification of the optimal treatment regimen in the presence of missing covariates.Statistics in medicine · 2020Article
- Multi-Armed Angle-Based Direct Learning for Estimating Optimal Individualized Treatment Rules With Various Outcomes.Journal of the American Statistical Association · 2020Article
- Effects of Proportional Hazard Assumption on Variable Selection Methods for Censored Data.Statistics in biopharmaceutical research · 2020Article
- Optimal two-stage dynamic treatment regimes from a classification perspective with censored survival data.Biometrics · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A treatment regime is a deterministic function that dictates personalized treatment based on patients' individual prognostic information. There is increasing interest in finding optimal treatment regimes, which determine treatment at one or more treatment decision points so as to maximize expected long-term clinical outcome, where larger outcomes are preferred. For chronic diseases such as cancer or HIV infection, survival time is often the outcome of interest, and the goal is to select treatment to maximize survival probability. We propose two nonparametric estimators for the survival function of patients following a given treatment regime involving one or more decisions, i.e., the so-called value. Based on data from a clinical or observational study, we estimate an optimal regime by maximizing these estimators for the value over a prespecified class of regimes. Because the value function is very jagged, we introduce kernel smoothing within the estimator to improve performance. Asymptotic properties of the proposed estimators of value functions are established under suitable regularity conditions, and simulations studies evaluate the finite-sample performance of the proposed regime estimators. The methods are illustrated by application to data from an AIDS clinical trial.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.