ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2017
Concordance-Assisted Learning for Estimating Optimal Individualized Treatment Regimes.
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 11 papers.
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The trial behind it
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
11 citing papers in PubMed.
- Optimizing Treatment Decision Estimation for Right-Censored Survival Data Through Parameter Transfer Learning.Statistics in medicine · 2026Article
- A RELUCTANT ADDITIVE MODEL FRAMEWORK FOR INTERPRETABLE NONLINEAR INDIVIDUALIZED TREATMENT RULES.The annals of applied statistics · 2023Article
- Semiparametric regression modeling of the global percentile outcome.Journal of statistical planning and inference · 2023Article
- A Single Index Model for Longitudinal Outcomes to Optimize Individual Treatment Decision Rules.Stat · 2022Article
- Multithreshold change plane model: Estimation theory and applications in subgroup identification.Statistics in medicine · 2021Article
- Statistical Inference for Online Decision-Making: In a Contextual Bandit Setting.Journal of the American Statistical Association · 2021Article
- Multi-Armed Angle-Based Direct Learning for Estimating Optimal Individualized Treatment Rules With Various Outcomes.Journal of the American Statistical Association · 2020Article
- ON TESTING CONDITIONAL QUALITATIVE TREATMENT EFFECTS.Annals of statistics · 2019Article
- Maximin Projection Learning for Optimal Treatment Decision with Heterogeneous Individualized Treatment Effects.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2018Article
- Estimation and evaluation of linear individualized treatment rules to guarantee performance.Biometrics · 2018Article
- Sparse concordance-assisted learning for optimal treatment decision.Journal of machine learning research : JMLR · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In this article, we propose a new concordance-assisted learning for estimating optimal individualized treatment regimes. We first introduce a type of concordance function for prescribing treatment and propose a robust rank regression method for estimating the concordance function. We then find treatment regimes, up to a threshold, to maximize the concordance function, named prescriptive index. Finally, within the class of treatment regimes that maximize the concordance function, we find the optimal threshold to maximize the value function. We establish the convergence rate and asymptotic normality of the proposed estimator for parameters in the prescriptive index. An induced smoothing method is developed to estimate the asymptotic variance of the proposed estimator. We also establish the
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Registered trials
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.