{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T13:04:19Z","timestamp":1789995859385,"version":"4.0.1"},"reference-count":28,"publisher":"Wiley","issue":"6-7","license":[{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"content-version":"vor","delay-in-days":15,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN\u201005896"],"award-info":[{"award-number":["RGPIN\u201005896"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100022210","name":"Canadian Statistical Sciences Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100022210","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012118","name":"Ontario Institute for Cancer Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012118","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":[[2026,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>\n                      In analysis of time\u2010to\u2010event outcomes, a mixture cure (MC) model is preferred over a standard survival model when the sample includes individuals who will never experience the event of interest. Motivated by a cohort study of breast cancer patients with incomplete biomarkers, we develop multiple imputation (MI) methods assuming a\n                      <jats:italic>Weibull<\/jats:italic>\n                      proportional hazards (PH\u2010MC) analysis model with multiple prognostic factors. However, for MI with fully conditional specification, an incorrectly specified imputation model can impair accuracy of point and interval estimates.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives and Methods<\/jats:title>\n                    <jats:p>Our goal is to propose imputation models that are compatible with the Weibull PH\u2010MC analysis models. We derive an exact conditional distribution (ECD) imputation model which involves the analysis model likelihood. Using simulation studies, we compare effect estimate bias and confidence interval (CI) coverage under alternative imputation models including the ECD model, an approximation that includes a cure indicator (cECD), and a comprehensive simple (CS) model. For robust parameter estimation in finite and\/or sparse samples, we incorporate the Firth\u2010type penalized likelihood (FT\u2010PL) and combined likelihood profile (CLIP) methods into the MI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Compared to complete case analysis, MI with penalization reduces estimation bias and improves coverage. Although ECD and cECD perform similarly at higher event rates, ECD generates smaller bias and higher coverage at lower rates. CS has larger bias and lower coverage than ECD and cECD, but CIs are narrower than for cECD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In analyses of biomarkers and composite subtypes for prognosis studies such as in breast cancer, the use of compatible imputation models and penalization methods is recommended for MC modeling in samples with low event numbers and\/or with covariate imbalance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/sim.70437","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T04:12:06Z","timestamp":1773720726000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Accommodating the Analysis Model in Multiple Imputation for the Weibull Mixture Cure Model: Performance Under Penalized Likelihood"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2884-8965","authenticated-orcid":false,"given":"Changchang","family":"Xu","sequence":"first","affiliation":[{"name":"Division of Biostatistics, Dalla Lana School of Public Health University of Toronto  Toronto Canada"},{"name":"Lunenfeld\u2010Tanenbaum Research Institute, Sinai Health  Toronto Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laurent","family":"Briollais","sequence":"additional","affiliation":[{"name":"Division of Biostatistics, Dalla Lana School of Public Health University of Toronto  Toronto Canada"},{"name":"Lunenfeld\u2010Tanenbaum Research Institute, Sinai Health  Toronto Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irene L.","family":"Andrulis","sequence":"additional","affiliation":[{"name":"Lunenfeld\u2010Tanenbaum Research Institute, Sinai Health  Toronto Ontario Canada"},{"name":"Department of Molecular Genetics, Temerty Faculty of Medicine University of Toronto  Toronto Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3280-7154","authenticated-orcid":false,"given":"Shelley B.","family":"Bull","sequence":"additional","affiliation":[{"name":"Division of Biostatistics, Dalla Lana School of Public Health University of Toronto  Toronto Canada"},{"name":"Lunenfeld\u2010Tanenbaum Research Institute, Sinai Health  Toronto Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,16]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1949.tb00020.x"},{"key":"e_1_2_9_3_1","first-page":"5","article-title":"Chapter 2: The Parametric Cure Models","volume":"1","author":"Peng Y.","year":"2021","journal-title":"Cure Models: Methods, Applications and 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