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Such data are appropriately analyzed using methods that account for this uncertainty in event time measurement. In this paper, we propose a survival tree method for interval\u2010censored data based on the conditional inference framework. Using Monte Carlo simulations, we find that the tree is effective in uncovering underlying tree structure, performs similarly to an interval\u2010censored Cox proportional hazards model fit when the true relationship is linear, and performs at least as well as (and in the presence of right\u2010censoring outperforms) the Cox model when the true relationship is not linear. Further, the interval\u2010censored tree outperforms survival trees based on imputing the event time as an endpoint or the midpoint of the censoring interval. We illustrate the application of the method on tooth emergence data.<\/jats:p>","DOI":"10.1002\/sim.7450","type":"journal-article","created":{"date-parts":[[2017,8,18]],"date-time":"2017-08-18T05:46:44Z","timestamp":1503035204000},"page":"4831-4842","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Survival trees for interval\u2010censored survival data"],"prefix":"10.1002","volume":"36","author":[{"given":"Wei","family":"Fu","sequence":"first","affiliation":[{"name":"Leonard N. Stern School of Business New York University New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1496-608X","authenticated-orcid":false,"given":"Jeffrey S.","family":"Simonoff","sequence":"additional","affiliation":[{"name":"Leonard N. 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