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

TL;DR

This paper introduces a Bayesian SDE model with covariates to analyze ambulatory blood pressure data, capturing its dynamic features despite irregular sampling, and demonstrates its application to real-world health data.

Contribution

It develops a novel Bayesian SDE framework with an efficient multiresolution algorithm for modeling irregularly spaced ambulatory blood pressure data incorporating covariates.

Findings

Age, caffeine, gender, and race influence blood pressure dynamics.

The model effectively captures morning surge and nighttime dipping.

The Bayesian approach improves parameter estimation accuracy.

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.