{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T09:20:29Z","timestamp":1759483229706,"version":"3.37.3"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2019,11,23]],"date-time":"2019-11-23T00:00:00Z","timestamp":1574467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Disease Society of America Human Biology Project Fellowship"},{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["K01NS099343"],"award-info":[{"award-number":["K01NS099343"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["R21DK103118"],"award-info":[{"award-number":["R21DK103118"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,17]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>In kin-cohort studies, clinicians want to provide their patients with the most current cumulative risk of death arising from a rare deleterious mutation. Estimating the cumulative risk is difficult when the genetic mutation status is unknown and only estimated probabilities of a patient having the mutation are available. We estimate the cumulative risk for this scenario using a novel nonparametric estimator that incorporates covariate information and dynamic landmark prediction. Our estimator has improved prediction accuracy over existing estimators that ignore covariate information. It is built within a dynamic landmark prediction framework whereby we can obtain personalized dynamic predictions over time. Compared to current standards, a simple transformation of our estimator provides more efficient estimates of marginal distribution functions in settings where patient-specific predictions are not the main goal. We show our estimator is unbiased and has more predictive accuracy compared to methods that ignore covariate information and landmarking. Applying our method to a Huntington disease study of mortality, we develop dynamic survival prediction curves incorporating gender and familial genetic information.<\/jats:p>","DOI":"10.1093\/biostatistics\/kxz052","type":"journal-article","created":{"date-parts":[[2019,11,6]],"date-time":"2019-11-06T12:13:42Z","timestamp":1573042422000},"page":"558-574","source":"Crossref","is-referenced-by-count":2,"title":["Dynamic landmark prediction for mixture data"],"prefix":"10.1093","volume":"22","author":[{"given":"Tanya P","family":"Garcia","sequence":"first","affiliation":[{"name":"Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843-3143, USA and RAND Corporation, 1776 Main Street, Santa Monica, CA 90401, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Layla","family":"Parast","sequence":"additional","affiliation":[{"name":"Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843-3143, USA and RAND 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