title: Bayesian Analysis of Ambulatory Blood Pressure Dynamics with Application to Irregularly Spaced Sparse Data.
abstract: Ambulatory cardiovascular (CV) measurements provide valuable insights into individuals' health conditions in "real-life," everyday settings. Current methods of modeling ambulatory CV data do not consider the dynamic characteristics of the full data set and their relationships with covariates such as caffeine use and stress. We propose a stochastic differential equation (SDE) in the form of a dual nonlinear Ornstein-Uhlenbeck (OU) model with person-specific covariates to capture the morning surge and nighttime dipping dynamics of ambulatory CV data. To circumvent the data analytic constraint that empirical measurements are typically collected at irregular and much larger time intervals than those evaluated in simulation studies of SDEs, we adopt a Bayesian approach with a regularized Brownian Bridge sampler (RBBS) and an efficient multiresolution (MR) algorithm to fit the proposed SDE. The MR algorithm can produce more efficient MCMC samples that is crucial for valid parameter estimation and inference. Using this model and algorithm to data from the Duke Behavioral Investigation of Hypertension Study, results indicate that age, caffeine intake, gender and race have effects on distinct dynamic characteristics of the participants' CV trajectories.
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Zhao-Hua
authors.initials: ZH
authors.lastname: Lu
authors.affiliation: Dept. of Human Development and Family Studies, Penn State University, 118 Henderson South Building, University Park, PA 16803.
authors.forename: Sy-Miin
authors.initials: SM
authors.lastname: Chow
authors.affiliation: Psychology and Neuroscience, Duke Universityz, 4569 Hosp South, Campus Box 3119 Med Ctr.
authors.forename: Andrew
authors.initials: A
authors.lastname: Sherwood
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Hongtu
authors.initials: H
authors.lastname: Zhu
keywords: Irregularly spaced longitudinal data; Latent process; Markov chain Monte Carlo; Multiresolution algorithm; Nonlinear process; Population estimation