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We build on recent work by Cand\u00e8s et\u00a0al. (2023), whose approach first subsets the data to discard any data points with early censoring times and then uses a reweighting technique, namely, weighted conformal inference (Tibshirani et\u00a0al., 2019), to correct for the distribution shift introduced by this subsetting procedure. For our new method, instead of constraining to a fixed threshold for the censoring time when subsetting the data, we allow for a covariate-dependent and data-adaptive subsetting step, which is better able to capture the heterogeneity of the censoring mechanism. As a result, our method can lead to lower predictive bounds that are less conservative and give more accurate information. We show that in the Type-I right-censoring setting, if either the censoring mechanism or the conditional quantile of the survival time is well estimated, our proposed procedure achieves nearly exact marginal coverage, where in the latter case we additionally have approximate conditional coverage. We evaluate the validity and efficiency of our proposed algorithm in numerical experiments, illustrating its advantage when compared with other competing methods. Finally, our method is applied to a real dataset to generate lower predictive bounds for users\u2019 active times on a mobile app.<\/jats:p>","DOI":"10.1093\/biomet\/asad076","type":"journal-article","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T23:59:57Z","timestamp":1701475197000},"page":"459-477","source":"Crossref","is-referenced-by-count":12,"title":["Conformalized survival analysis with adaptive cut-offs"],"prefix":"10.1093","volume":"111","author":[{"given":"Yu","family":"Gui","sequence":"first","affiliation":[{"name":"Department of Statistics, University of Chicago , 5747 South Ellis Avenue , Chicago, Illinois 60637, U.S.A. yugui@uchicago.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohan","family":"Hore","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Chicago , 5747 South Ellis Avenue , Chicago, Illinois 60637, U.S.A. rohanhere@uchicago.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2872-5842","authenticated-orcid":false,"given":"Zhimei","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, The Wharton School, University of Pennsylvania , 265 South 37th Street , Philadelphia, Pennsylvania 19104, U.S.A. zren@wharton.upenn.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4854-8644","authenticated-orcid":false,"given":"Rina Foygel","family":"Barber","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Chicago , 5747 South Ellis Avenue , Chicago, Illinois 60637, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"2024121017570242600_asad076-B1","author":"Angelopoulos","year":"2022"},{"key":"2024121017570242600_asad076-B2","author":"Angelopoulos","year":"2023"},{"key":"2024121017570242600_asad076-B3","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1214\/18-AOS1709","article-title":"Generalized random forests","volume":"47","author":"Athey","year":"2019","journal-title":"Ann. 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