{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:49:16Z","timestamp":1768956556940,"version":"3.49.0"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2019,8,2]],"date-time":"2019-08-02T00:00:00Z","timestamp":1564704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Fondation HEC Montr\u00e9al"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,1,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Personalized medicine often relies on accurate estimation of a treatment effect for specific subjects. This estimation can be based on the subject\u2019s baseline covariates but additional complications arise for a time-to-event response subject to censoring. In this paper, the treatment effect is measured as the difference between the mean survival time of a treated subject and the mean survival time of a control subject. We propose a new random forest method for estimating the individual treatment effect with survival data. The random forest is formed by individual trees built with a splitting rule specifically designed to partition the data according to the individual treatment effect. For a new subject, the forest provides a set of similar subjects from the training dataset that can be used to compute an estimation of the individual treatment effect with any adequate method.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The merits of the proposed method are investigated with a simulation study where it is compared to numerous competitors, including recent state-of-the-art methods. The results indicate that the proposed method has a very good and stable performance to estimate the individual treatment effects. Two examples of application with a colon cancer data and breast cancer data show that the proposed method can detect a treatment effect in a sub-population even when the overall effect is small or nonexistent.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The authors are working on an R package implementing the proposed method and it will be available soon. In the meantime, the code can be obtained from the first author at sami.tabib@hec.ca.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz602","type":"journal-article","created":{"date-parts":[[2019,7,30]],"date-time":"2019-07-30T19:10:13Z","timestamp":1564513813000},"page":"629-636","source":"Crossref","is-referenced-by-count":18,"title":["Non-parametric individual treatment effect estimation for survival data with random forests"],"prefix":"10.1093","volume":"36","author":[{"given":"Sami","family":"Tabib","sequence":"first","affiliation":[{"name":"Department of Decision Sciences, HEC Montr\u00e9al, Montr\u00e9al , QC H3T 2A7, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7372-7943","authenticated-orcid":false,"given":"Denis","family":"Larocque","sequence":"additional","affiliation":[{"name":"Department of Decision Sciences, HEC Montr\u00e9al, Montr\u00e9al , QC H3T 2A7, Canada"}]}],"member":"286","published-online":{"date-parts":[[2019,8,2]]},"reference":[{"key":"2023013112062320500_btz602-B1","doi-asserted-by":"crossref","first-page":"2669","DOI":"10.1002\/sim.7297","article-title":"Causal inference in survival analysis using pseudo-observations","volume":"36","author":"Andersen","year":"2017","journal-title":"Stat. 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