{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T00:21:08Z","timestamp":1791937268161,"version":"4.3.4"},"reference-count":39,"publisher":"Wiley","issue":"10-12","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Advancements in modern medical technology have enabled cures for a fraction of patients while extending survival times for those who are not cured. For non\u2010cured patients, disease recurrence is influenced by observed covariates and unobserved individual heterogeneity (random effects). In biomedical studies, dependent censoring is frequently encountered, for example, in cancer patients, where right censoring can be caused by death from unrelated diseases or due to an (unobservable) cure status. This study introduces a joint frailty model for recurrent event data with a cure fraction, effectively capturing heterogeneity and inducing dependent censoring. The proposed multivariate joint frailty mixture cure models incorporate covariates and frailties, together with the event incidence time and latent cure status. The model accounts for the probability of a cure after each recurrence using both the complementary log\u2013log and the logistic link function. A likelihood\u2010based estimation method is developed using the Monte Carlo Expectation\u2010Maximization (MCEM) algorithm. Through Monte Carlo simulation, we examine the finite sample properties of the MCEM estimators, supplemented by a real\u2010world application using secondary data on hospital readmissions for colorectal cancer recurrence post\u2010surgery. Simulation results suggest lifetime and frailty parameter estimates are unbiased and consistent. Compared to models with identical frailty structure, both the complementary log\u2013log and the logistic cure frailty models with dependent frailties demonstrate a better fit with the real data, as evidenced by lower Akaike information criteria values.<\/jats:p>","DOI":"10.1002\/sim.70579","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T13:18:28Z","timestamp":1778246308000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Joint Frailty Mixture Cure Model for Recurrent Event Data With Dependent Censoring: An MCEM Approach"],"prefix":"10.1002","volume":"45","author":[{"given":"Nasrin","family":"Sultana","sequence":"first","affiliation":[{"name":"Applied Statistics and Data Science Institute of Statistical Research and Training, University of Dhaka  Dhaka Bangladesh"},{"name":"Department of Agricultural and Applied Statistics Gazipur Agricultural University  Gazipur Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3183-3756","authenticated-orcid":false,"given":"Moudud","family":"Alam","sequence":"additional","affiliation":[{"name":"School of Information and Engineering Dalarna University  Falun Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8405-4361","authenticated-orcid":false,"given":"Md","family":"Hasinur Rahaman Khan","sequence":"additional","affiliation":[{"name":"Applied Statistics and Data Science Institute of Statistical Research and Training, University of Dhaka  Dhaka Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9876.00409"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxl043"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.13202"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2007.00589.x"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280210395521"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006-341X.2002.00510.x"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.1801"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006-341X.2002.00079.x"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.201800391"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022978802021"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4479"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280220921889"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/79.3.531"},{"key":"e_1_2_10_15_1","volume-title":"Survival Analysis With Long\u2010Term Survivors","author":"Maller R. 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