{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T12:55:44Z","timestamp":1789217744654,"version":"build-2803163510"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"content-version":"vor","delay-in-days":1747,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["NSF-DMS-1555244"],"award-info":[{"award-number":["NSF-DMS-1555244"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["NCI P01 CA142538"],"award-info":[{"award-number":["NCI P01 CA142538"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,9,1]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>A salient feature of data from clinical trials and medical studies is inhomogeneity. Patients not only differ in baseline characteristics, but also in the way that 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. We mainly consider patients\u2019 heterogeneity caused by groupwise individualized treatment effects assuming the same marginal treatment effects for all groups. We propose a new maximin projection learning method 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 problem, which can be efficiently computed by interior point methods. Consistency and asymptotic normality of the estimator are established. Numerical examples show the reliability of the methodology proposed.<\/jats:p>","DOI":"10.1111\/rssb.12273","type":"journal-article","created":{"date-parts":[[2018,5,11]],"date-time":"2018-05-11T02:11:34Z","timestamp":1526004694000},"page":"681-702","source":"Crossref","is-referenced-by-count":19,"title":["Maximin Projection Learning for Optimal Treatment Decision with Heterogeneous Individualized Treatment Effects"],"prefix":"10.1093","volume":"80","author":[{"given":"Chengchun","family":"Shi","sequence":"first","affiliation":[{"name":"North Carolina State University , Raleigh , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Song","sequence":"additional","affiliation":[{"name":"Fudan University , Shanghai , People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbin","family":"Lu","sequence":"additional","affiliation":[{"name":"North Carolina State University , Raleigh , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Fu","sequence":"additional","affiliation":[{"name":"North Carolina State University , Raleigh , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2018,5,10]]},"reference":[{"key":"2023022101105156500_","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1109\/34.385977","article-title":"Multiple subclass pattern recognition: a maximin correlation approach","volume":"17","author":"Avi-Itzhak","year":"1995","journal-title":"IEEE Trans. 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