{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T20:22:15Z","timestamp":1790713335449,"version":"4.1.0"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T00:00:00Z","timestamp":1678665600000},"content-version":"vor","delay-in-days":3407,"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":["DMS-1106576"],"award-info":[{"award-number":["DMS-1106576"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University of Minnesota Supercomputing Institute"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,9,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>Researchers often believe that a treatment's effect on a response may be heterogeneous with respect to certain baseline covariates. This is an important premise of personalized medicine. Several methods for estimating heterogeneous treatment effects have been proposed. However, little attention has been given to the problem of choosing between estimators of treatment effects. Models that best estimate the regression function may not be best for estimating the effect of a treatment; therefore, there is a need for model selection methods that are targeted to treatment effect estimation. We demonstrate an application of the focused information criterion in this setting and develop a treatment effect cross-validation aimed at minimizing treatment effect estimation errors. Theoretically, treatment effect cross-validation has a model selection consistency property when the data splitting ratio is properly chosen. Practically, treatment effect cross-validation has the flexibility to compare different types of models. We illustrate the methods by using simulation studies and data from a clinical trial comparing treatments of patients with human immunodeficiency virus.<\/jats:p>","DOI":"10.1111\/rssb.12043","type":"journal-article","created":{"date-parts":[[2013,11,13]],"date-time":"2013-11-13T03:39:43Z","timestamp":1384313983000},"page":"749-769","source":"Crossref","is-referenced-by-count":41,"title":["Model Selection for Estimating Treatment Effects"],"prefix":"10.1093","volume":"76","author":[{"given":"Craig A.","family":"Rolling","sequence":"first","affiliation":[{"name":"University of Minnesota , Minneapolis , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhong","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Minnesota , Minneapolis , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2013,11,13]]},"reference":[{"key":"2023031400062933100_","doi-asserted-by":"crossref","first-page":"e18578","DOI":"10.1371\/journal.pone.0018578","article-title":"Development and validation of decision rules to guide frequency of monitoring CD4 cell count in HIV-1 infection before starting antiretroviral therapy","volume":"6","author":"Buclin","year":"2011","journal-title":"PLOS ONE"},{"key":"2023031400062933100_","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1093\/biostatistics\/kxq060","article-title":"Analysis of randomized comparative clinical trial data for personalized treatment selections","volume":"12","author":"Cai","year":"2011","journal-title":"Biostatistics"},{"key":"2023031400062933100_","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1198\/016214503000000819","article-title":"The focused information criterion","volume":"98","author":"Claeskens","year":"2003","journal-title":"J. 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