{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T21:21:56Z","timestamp":1777929716519,"version":"3.51.4"},"reference-count":27,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2017,5,19]],"date-time":"2017-05-19T00:00:00Z","timestamp":1495152000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Stat Methods Med Res"],"published-print":{"date-parts":[[2017,10]]},"abstract":"<jats:p>Prediction accuracy of a cure model when it is used to predict the cure probability of a patient is an important but not well-addressed issue in survival analysis. We propose a method to assess the prediction accuracy of a mixture cure model in predicting cure probability based on inverse probability of censoring weights to incorporate the censoring and latent cure status in the data. The inverse probability of censoring weight-adjusted estimator is shown to be consistent for the true expected prediction error for cure probability. A simulation study shows that the estimator performs well with finite samples when subjects with censored survival times greater than the largest uncensored time are identified as cured, an approach that is often used in mixture cure model literature to increase model identifiability. The simulation study also investigates the performance of the estimator with different thresholds to identify cured subjects and the estimator based on observed (training) data only. The method is applied to bone barrow transplant data for leukemia patients for assessing prediction accuracy for the cure probabilities.<\/jats:p>","DOI":"10.1177\/0962280217708673","type":"journal-article","created":{"date-parts":[[2017,5,19]],"date-time":"2017-05-19T05:47:44Z","timestamp":1495172864000},"page":"2029-2041","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":12,"title":["Prediction accuracy for the cure probabilities in mixture cure models"],"prefix":"10.1177","volume":"26","author":[{"given":"Wenyu","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Mathematics and Statistics, Queen\u2019s University, Kingston, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoyu","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Queen\u2019s University, Kingston, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingwei","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Queen\u2019s University, Kingston, ON, Canada"},{"name":"Department of Public Health Sciences, Queen\u2019s University, Kingston, ON, Canada"},{"name":"Division of Cancer Care and Epidemiology, Queen's Cancer Research Institute, Kingston, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2017,5,19]]},"reference":[{"key":"bibr1-0962280217708673","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1111\/j.2517-6161.1949.tb00020.x","volume":"11","author":"Boag JW","year":"1949","journal-title":"J R Stat Soc"},{"key":"bibr2-0962280217708673","doi-asserted-by":"publisher","DOI":"10.2307\/3314804"},{"key":"bibr3-0962280217708673","volume-title":"Survival analysis with long-term survivors","author":"Maller RA","year":"1996"},{"key":"bibr4-0962280217708673","unstructured":"Peng Y and Taylor JMG. 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