Wu, YingCook, Richard2022-08-082022-08-082022-01https://doi.org/10.1093/biostatistics/kxaa011http://hdl.handle.net/10012/18493This is a pre-copyedited, author-produced PDF of an article accepted for publication in Biostatistics following peer review. The version records “Wu Y and Cook RJ (2022), Assessing the accuracy of predictive models with interval-censored data, Biostatistics, 23 (1): 18–33”. DOI: 10.1093/biostatistics/kxaa011 is available online at: https://doi.org/10.1093/biostatistics/kxaa011.We develop methods for assessing the predictive accuracy of a given event time model when the validation sample is comprised of case K interval-censored data. An imputation-based, an inverse probability weighted (IPW), and an augmented inverse probability weighted (AIPW) estimator are developed and evaluated for the mean prediction error and the area under the receiver operating characteristic curve when the goal is to predict event status at a landmark time. The weights used for the IPW and AIPW estimators are obtained by fitting a multistate model which jointly considers the event process, the recurrent assessment process, and loss to follow-up. We empirically investigate the performance of the proposed methods and illustrate their application in the context of a motivating rheumatology study in which human leukocyte antigen markers are used to predict disease progression status in patients with psoriatic arthritis.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Augmented inverse probability weighted estimator, intermittent assessment, interval censoring, inverse probability weighted estimator, prediction error, ROC curveArticle