ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2018
Maximin Projection Learning for Optimal Treatment Decision with Heterogeneous Individualized Treatment Effects.
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
8 citing papers in PubMed.
- Robust transfer learning for individualized treatment rules in the presence of missing data.Biostatistics (Oxford, England) · 2025Article
- Sparse 2-stage Bayesian meta-analysis for individualized treatments.Biometrics · 2025Article
- Learn-As-you-GO (LAGO) trials: optimizing treatments and preventing trial failure through ongoing learning.Biometrics · 2025Article
- Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies.Journal of the American Statistical Association · 2024Article
- Penalized robust learning for optimal treatment regimes with heterogeneous individualized treatment effects.Journal of applied statistics · 2024Article
- SurvMaximin: Robust federated approach to transporting survival risk prediction models.Journal of biomedical informatics · 2022Article
- Multiple domain and multiple kernel outcome-weighted learning for estimating individualized treatment regimes.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2022Article
- Learning Optimal Distributionally Robust Individualized Treatment Rules.Journal of the American Statistical Association · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A saline feature of data from clinical trials and medical studies is inhomogeneity. Patients not only differ in baseline characteristics, but also the way they respond to treatment. Optimal individualized treatment regimes are developed to select effective treatments based on patient's heterogeneity. However, the optimal treatment regime might also vary for patients across different subgroups. In this paper, we mainly consider patient's heterogeneity caused by groupwise individualized treatment effects assuming the same marginal treatment effects for all groups. We propose a new maximin-projection learning for estimating a single treatment decision rule that works reliably for a group of future patients from a possibly new subpopulation. Based on estimated optimal treatment regimes for all subgroups, the proposed maximin treatment regime is obtained by solving a quadratically constrained linear programming (QCLP) problem, which can be efficiently computed by interior-point methods. Consistency and asymptotic normality of the estimator is established. Numerical examples show the reliability of the proposed methodology.
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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.