{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T14:44:15Z","timestamp":1792593855836,"version":"4.5.0"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"1","funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,20]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>Understanding the population-level natural history of human papillomavirus (HPV) infection and cervical precancer is essential for informing effective prevention strategies. Multistate models provide a principled framework for characterizing disease progression by modeling transitions among discrete health states. However, intensity-based multistate analyses typically require longitudinal data, which are often costly and logistically difficult to obtain, whereas cross-sectional data are more widely available, particularly in resource-limited settings. We develop a semi-Markov multistate model that captures HPV infection dynamics and cervical precancer progression, including recurrent transitions between HPV-infected and uninfected states. To enable estimation from cross-sectional data, we propose a two-step approach that combines local cross-sectional observations with external information on transferable transition intensities. First, biologically driven transition intensities are estimated from external data under a transferability assumption. Second, these estimates are incorporated into an approximate likelihood to infer population-specific transition intensities shaped by local epidemiological and behavioral factors. Simulation studies and applications to 2 populations demonstrate the performance of the proposed method and its practical utility.<\/jats:p>","DOI":"10.1093\/biostatistics\/kxag022","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T09:33:41Z","timestamp":1783676021000},"source":"Crossref","is-referenced-by-count":0,"title":["Data-augmented multistate modeling for chronic disease processes using cross-sectional studies: application to HPV and cervical precancer"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1162-4348","authenticated-orcid":false,"given":"Fangya","family":"Mao","sequence":"first","affiliation":[{"name":"Division of Cancer Epidemiology and Genetics, National Cancer Institute Biostatistics Branch, , 9609 Medical Center Drive , Rockville, MD, 20850,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicole G","family":"Campos","sequence":"additional","affiliation":[{"name":"Harvard T.H. Chan School of Public Health Center for Health Decision Science, , 677 Huntington Avenue , Boston, MA, 02115,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1625-4331","authenticated-orcid":false,"given":"Li C","family":"Cheung","sequence":"additional","affiliation":[{"name":"Division of Cancer Epidemiology and Genetics, National Cancer Institute Biostatistics Branch, , 9609 Medical Center Drive , Rockville, MD, 20850,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,7,9]]},"reference":[{"key":"2026102110244959600_kxag022-B1","doi-asserted-by":"publisher","first-page":"920","DOI":"10.1002\/ijc.34128","article-title":"Different human pcapillomavirus types share early natural history transitions in immunocompetent women","volume":"151","author":"Adebamowo","year":"2022","journal-title":"Int J 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