{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T02:48:17Z","timestamp":1792118897142,"version":"4.4.0"},"reference-count":45,"publisher":"Wiley","issue":"23-24","license":[{"start":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:00:00Z","timestamp":1760227200000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100012325","name":"National Office for Philosophy and Social Sciences","doi-asserted-by":"publisher","award":["21CTJ009"],"award-info":[{"award-number":["21CTJ009"]}],"id":[{"id":"10.13039\/501100012325","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Given the increasing interest focus on personalized medicine, a number of advanced statistical methods have been developed for estimating heterogeneous treatment effects (HTEs). However, methods for estimating HTEs in medical applications are limited, as they often involve potentially censored and heteroskedastic survival outcomes. Ignoring censoring and heteroskedasticity may introduce bias into HTEs. Therefore, in this study, we proposed two doubly robust (DR) methods for estimating HTEs based on nonparametric failure time (NFT) Bayesian additive regression trees (BART). Our contributions are as follows: (1) by using NFT BART as the prediction model, we avoid many restrictive assumptions, such as linearity, proportional hazards, and homoscedasticity; (2) we extend the DR\u2010Learner to survival data, allowing it to handle the common issue of censoring and confounding in observational data; (3) we conduct a comprehensive simulation study of the present HTEs estimation strategies using several data generation processes in which we systematically vary the sample size of the training set, treatment\u2010specific propensity score distribution, censoring rate, unbalanced treatment assignment, complexity of the model and bias function, and heteroskedastic or homoscedastic outcome. Through simulations, we demonstrate the effectiveness and robustness of the two proposed approaches in estimating HTEs. We also conduct a real data application of individualized hypertension management on observational data from the National Health and Nutrition Examination Survey (NHANES). Consequently, the proposed methods could yield robust estimates of HTE in observational survival data.<\/jats:p>","DOI":"10.1002\/sim.70301","type":"journal-article","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T04:50:32Z","timestamp":1760331032000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Doubly Robust Estimators for Heterogeneous Treatment Effects in Heteroskedastic Survival Data"],"prefix":"10.1002","volume":"44","author":[{"given":"Yuhui","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health Xi'an Jiaotong University Health Science Center  Xi'an Shaanxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health Xi'an Jiaotong University Health Science Center  Xi'an Shaanxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenli","family":"Liao","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health Xi'an Jiaotong University Health Science Center  Xi'an Shaanxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangyao","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health Xi'an Jiaotong University Health Science Center  Xi'an Shaanxi China"},{"name":"Department of Radiology First Affiliated Hospital of Xi'an Jiaotong University  Xi'an Shaanxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,10,12]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0887\u2010378X.2004.00327.x"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev\u2010statistics\u2010030718\u2010105251"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aal4321"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/1745\u20106215\u201011\u201085"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.13857"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.8357"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.9090"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/jrsssb\/qkac001"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.10180"},{"key":"e_1_2_13_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591\u2010024\u201002902\u20101"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxy028"},{"key":"e_1_2_13_13_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1319839"},{"key":"e_1_2_13_14_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1804597116"},{"key":"e_1_2_13_15_1","unstructured":"A.CurthandM.van derSchaar \u201cNonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms \u201d2021 accessed May 6 2024 http:\/\/arxiv.org\/abs\/2101.10943."},{"key":"e_1_2_13_16_1","doi-asserted-by":"publisher","DOI":"10.1214\/23\u2010EJS2157"},{"key":"e_1_2_13_17_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asaa076"},{"key":"e_1_2_13_18_1","doi-asserted-by":"publisher","DOI":"10.1214\/23\u2010AOS2258"},{"key":"e_1_2_13_19_1","doi-asserted-by":"publisher","DOI":"10.1111\/ectj.12097"},{"key":"e_1_2_13_20_1","doi-asserted-by":"publisher","DOI":"10.1037\/h0037350"},{"key":"e_1_2_13_21_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/70.1.41"},{"key":"e_1_2_13_22_1","unstructured":"P.Morzywolek J.Decruyenaere andS.Vansteelandt \u201cOn Weighted Orthogonal Learners for Heterogeneous Treatment Effects \u201d2024 accessed October 29 2024 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