Semiparametric Stochastic Modeling of the Rate Function in Longitudinal Studies.

ABSTRACT: In longitudinal biomedical studies, there is often interest in the rate functions, which describe the functional rates of change of biomarker profiles. This paper proposes a semiparametric approach to model these functions as the realizations of stochastic processes defined by stochastic differential equations. These processes are dependent on the covariates of interest and vary around a specified parametric function. An efficient Markov chain Monte Carlo algorithm is developed for inference. The proposed method is compared with several existing methods in terms of goodness-of-fit and more importantly the ability to forecast future functional data in a simulation study. The proposed methodology is applied to prostate-specific antigen profiles for illustration. Supplementary materials for t

SUBMITTER: Zhu B 

PROVIDER: S-EPMC3298426 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature