Open Access
September 2015 Bayesian analysis of ambulatory blood pressure dynamics with application to irregularly spaced sparse data
Zhao-Hua Lu, Sy-Miin Chow, Andrew Sherwood, Hongtu Zhu
Ann. Appl. Stat. 9(3): 1601-1620 (September 2015). DOI: 10.1214/15-AOAS846


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.


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Zhao-Hua Lu. Sy-Miin Chow. Andrew Sherwood. Hongtu Zhu. "Bayesian analysis of ambulatory blood pressure dynamics with application to irregularly spaced sparse data." Ann. Appl. Stat. 9 (3) 1601 - 1620, September 2015.


Received: 1 June 2014; Revised: 1 February 2015; Published: September 2015
First available in Project Euclid: 2 November 2015

zbMATH: 06526000
MathSciNet: MR3418737
Digital Object Identifier: 10.1214/15-AOAS846

Keywords: Irregularly spaced longitudinal data , latent process , Markov chain Monte Carlo , multiresolution algorithm , nonlinear process , population estimation

Rights: Copyright © 2015 Institute of Mathematical Statistics

Vol.9 • No. 3 • September 2015
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