ArticleThe annals of applied statistics2015
Bayesian Analysis of Ambulatory Blood Pressure Dynamics with Application to Irregularly Spaced Sparse Data.
Article in The annals of applied statistics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Estimation of nonlinear mixed-effects continuous-time models using the continuous-discrete extended Kalman filter.The British journal of mathematical and statistical psychology · 2023Article
- Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates.Psychometrika · 2022Article
- Dynamic models of stress-smoking responses based on high-frequency sensor data.NPJ digital medicine · 2021Article
- Identifying Qualitative Between-Subject and Within-Subject Variability: A Method for Clustering Regime-Switching Dynamics.Frontiers in psychology · 2020Article
- Zero-Inflated Regime-Switching Stochastic Differential Equation Models for Highly Unbalanced Multivariate, Multi-Subject Time-Series Data.Psychometrika · 2019Article
- What's for dynr: A Package for Linear and Nonlinear Dynamic Modeling in R.The R journal · 2019Article
- Representing Sudden Shifts in Intensive Dyadic Interaction Data Using Differential Equation Models with Regime Switching.Psychometrika · 2018Article
- Multilevel Latent Differential Structural Equation Model with Short Time Series and Time-Varying Covariates: A Comparison of Frequentist and Bayesian Estimators.Multivariate behavioral researchArticle
- Practical Tools and Guidelines for Exploring and Fitting Linear and Nonlinear Dynamical Systems Models.Multivariate behavioral researchArticle
- Fitting Bayesian Stochastic Differential Equation Models with Mixed Effects through a Filtering Approach.Multivariate behavioral researchArticle
- A Comparison of Two-Stage Approaches for Fitting Nonlinear Ordinary Differential Equation Models with Mixed Effects.Multivariate behavioral researchArticle
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4 authors.
Funding
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.
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