Bayesian analysis of ambulatory blood pressure dynamics with application to irregularly spaced sparse data

Zhao Hua Lu, Sy-Miin Chow, Andrew Sherwood, Hongtu Zhu

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1601-1620
Number of pages20
JournalAnnals of Applied Statistics
Volume9
Issue number3
DOIs
StatePublished - Sep 1 2015

Fingerprint

Sparse Data
Blood pressure
Blood Pressure
Bayesian Analysis
Caffeine
Differential equations
Dynamic Characteristics
Multiresolution
Stochastic Equations
Covariates
Parameter estimation
Differential equation
Brownian Bridge
Trajectories
Health
Hypertension
Surge
Markov Chain Monte Carlo
Bayesian Approach
Parameter Estimation

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

Cite this

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Bayesian analysis of ambulatory blood pressure dynamics with application to irregularly spaced sparse data. / Lu, Zhao Hua; Chow, Sy-Miin; Sherwood, Andrew; Zhu, Hongtu.

In: Annals of Applied Statistics, Vol. 9, No. 3, 01.09.2015, p. 1601-1620.

Research output: Contribution to journalArticle

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