{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T06:54:23Z","timestamp":1790405663617,"version":"4.1.0"},"reference-count":30,"publisher":"Wiley","issue":"21","license":[{"start":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T00:00:00Z","timestamp":1721606400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100009633","name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","doi-asserted-by":"publisher","award":["F31HD102159"],"award-info":[{"award-number":["F31HD102159"]}],"id":[{"id":"10.13039\/100009633","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2024,9,20]]},"abstract":"<jats:p>Preeclampsia is a pregnancy\u2010associated condition posing risks of both fetal and maternal mortality and morbidity that can only resolve following delivery and removal of the placenta. Because in its typical form preeclampsia can arise before delivery, but not after, these two events exemplify the time\u2010to\u2010event setting of \u201csemi\u2010competing risks\u201d in which a non\u2010terminal event of interest is subject to the occurrence of a terminal event of interest. The semi\u2010competing risks framework presents a valuable opportunity to simultaneously address two clinically meaningful risk modeling tasks: (i) characterizing risk of developing preeclampsia, and (ii) characterizing time to delivery after onset of preeclampsia. However, some people with preeclampsia deliver immediately upon diagnosis, while others are admitted and monitored for an extended period before giving birth, resulting in two distinct trajectories following the non\u2010terminal event, which we call \u201cclinically immediate\u201d and \u201cnon\u2010immediate\u201d terminal events. Though such phenomena arise in many clinical contexts, to\u2010date there have not been methods developed to acknowledge the complex dependencies between such outcomes, nor leverage these phenomena to gain new insight into individualized risk. We address this gap by proposing a novel augmented frailty\u2010based illness\u2010death model with a binary submodel to distinguish risk of immediate terminal event following the non\u2010terminal event. The model admits direct dependence of the terminal event on the non\u2010terminal event through flexible regression specification, as well as indirect dependence via a shared frailty term linking each submodel. We develop an efficient Bayesian sampler for estimation and corresponding model fit metrics, and derive formulae for dynamic risk prediction. In an extended example using pregnancy outcome data from an electronic health record, we demonstrate the proposed model's direct applicability to address a broad range of clinical questions.<\/jats:p>","DOI":"10.1002\/sim.10181","type":"journal-article","created":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T02:31:04Z","timestamp":1721701864000},"page":"4194-4211","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An augmented illness\u2010death model for semi\u2010competing risks with clinically immediate terminal events"],"prefix":"10.1002","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8813-9127","authenticated-orcid":false,"given":"Harrison T.","family":"Reeder","sequence":"first","affiliation":[{"name":"Biostatistics Massachusetts General Hospital  Boston Massachusetts"},{"name":"Department of Medicine Harvard Medical School  Boston Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyu Ha","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Nutrition Harvard T.H. Chan School of Public Health  Boston Massachusetts"},{"name":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston Massachusetts"},{"name":"Department of Biostatistics Harvard T.H. Chan School of Public Health  Boston Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefania I.","family":"Papatheodorou","sequence":"additional","affiliation":[{"name":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston Massachusetts"},{"name":"Department of Biostatistics and Epidemiology Rutgers University  Newark New Jersey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastien","family":"Haneuse","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Harvard T.H. 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