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The functional linear Cox regression model was used to establish that functional covariates including hippocampus surface morphology and scalar covariates including brain MRI volumes, cognitive performance (ADAS-Cog) and APOE-$\\varepsilon4$ status can accurately predict time to onset of AD. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFLCRM.<\/jats:p>","DOI":"10.1214\/15-aoas879","type":"journal-article","created":{"date-parts":[[2016,1,28]],"date-time":"2016-01-28T10:32:08Z","timestamp":1453977128000},"page":"2153-2178","source":"Crossref","is-referenced-by-count":38,"title":["BFLCRM: A Bayesian functional linear Cox regression model for predicting time to conversion to Alzheimer\u2019s disease"],"prefix":"10.1214","volume":"9","author":[{"given":"Eunjee","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongtu","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dehan","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yalin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kelly","family":"Sullivan Giovanello","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph G.","family":"Ibrahim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"for the Alzheimer's Disease","family":"Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"108","reference":[{"key":"72","doi-asserted-by":"publisher","unstructured":"Spiegelhalter, D. 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