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 Browni

SUBMITTER: Lu ZH 

PROVIDER: S-EPMC4773035 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature