A Bayesian nonlinear mixed-effects disease progression model.
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ABSTRACT:
SUBMITTER: Kim S
PROVIDER: S-EPMC4718583 | biostudies-literature | 2015 Dec
REPOSITORIES: biostudies-literature
Publications
A Bayesian nonlinear mixed-effects disease progression model.
Kim Seongho S Jang Hyejeong H Wu Dongfeng D Abrams Judith J
Journal of biometrics & biostatistics 20151230 5
A nonlinear mixed-effects approach is developed for disease progression models that incorporate variation in age in a Bayesian framework. We further generalize the probability model for sensitivity to depend on age at diagnosis, time spent in the preclinical state and sojourn time. The developed models are then applied to the Johns Hopkins Lung Project data and the Health Insurance Plan for Greater New York data using Bayesian Markov chain Monte Carlo and are compared with the estimation method ...[more]
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