{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T16:01:21Z","timestamp":1747152081566,"version":"3.40.5"},"reference-count":19,"publisher":"Wiley","issue":"18","license":[{"start":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T00:00:00Z","timestamp":1717977600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2024,8,15]]},"abstract":"<jats:p>Analysis of competing risks data has been an important topic in survival analysis due to the need to account for the dependence among the competing events. Also, event times are often recorded on discrete time scales, rendering the models tailored for discrete\u2010time nature useful in the practice of survival analysis. In this work, we focus on regression analysis with discrete\u2010time competing risks data, and consider the errors\u2010in\u2010variables issue where the covariates are prone to measurement errors. Viewing the true covariate value as a parameter, we develop the conditional score methods for various discrete\u2010time competing risks models, including the cause\u2010specific and subdistribution hazards models that have been popular in competing risks data analysis. The proposed estimators can be implemented by efficient computation algorithms, and the associated large sample theories can be simply obtained. Simulation results show satisfactory finite sample performances, and the application with the competing risks data from the scleroderma lung study reveals the utility of the proposed methods.<\/jats:p>","DOI":"10.1002\/sim.10098","type":"journal-article","created":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T22:30:25Z","timestamp":1718058625000},"page":"3503-3523","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Conditional score approaches to errors\u2010in\u2010variables competing risks data in discrete time"],"prefix":"10.1002","volume":"43","author":[{"given":"Chi\u2010Chung","family":"Wen","sequence":"first","affiliation":[{"name":"Department of Mathematics Tamkang University  New Taipei Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4038-9439","authenticated-orcid":false,"given":"Yi\u2010Hau","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Statistical Science Academia Sinica  Taipei Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,6,10]]},"reference":[{"volume-title":"The Statistical Analysis of Failure Time Data","year":"2011","author":"Kalbfleisch JD","key":"e_1_2_9_2_1"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.12881"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-28158-2"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.2307\/2530374"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1999.10474144"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.2307\/2347464"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.00204"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9469.2007.00567.x"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2009.00734.x"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxy069"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-2600(16)30152-7"},{"issue":"4","key":"e_1_2_9_13_1","first-page":"703","article-title":"Conditional scores and optimal scores for generalized linear measurement\u2010error models","volume":"74","author":"Stefanski LA","year":"1987","journal-title":"Biometrika"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.8729"},{"volume-title":"Statistical Inference","year":"2021","author":"Casella G","key":"e_1_2_9_15_1"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781420010138"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/88.2.447"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1995.10476629"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1996.10476682"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1214\/08-EJS341"}],"container-title":["Statistics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/sim.10098","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T05:41:40Z","timestamp":1729057300000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/sim.10098"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,10]]},"references-count":19,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2024,8,15]]}},"alternative-id":["10.1002\/sim.10098"],"URL":"https:\/\/doi.org\/10.1002\/sim.10098","archive":["Portico"],"relation":{},"ISSN":["0277-6715","1097-0258"],"issn-type":[{"type":"print","value":"0277-6715"},{"type":"electronic","value":"1097-0258"}],"subject":[],"published":{"date-parts":[[2024,6,10]]},"assertion":[{"value":"2023-03-31","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-20","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}