A Two-Dimensional Intrinsic Gaussian Markov Random Field for Blood Pressure Data

TL;DR

This paper introduces a Bayesian hierarchical model utilizing a two-dimensional Intrinsic Gaussian Markov Random Field to estimate and analyze blood pressure data's non-linear trends and interactions across countries and time.

Contribution

It develops a novel 2D second-order IGMRF model for bivariate data, enhancing the analysis of complex health indicators like blood pressure.

Findings

Effective in capturing non-linear trends

Performs well on simulated data

Provides insights into blood pressure interactions

Abstract

Many real-world phenomena are naturally bivariate. This includes blood pressure, which comprises systolic and diastolic levels. Here, we develop a Bayesian hierarchical model that estimates these values and their interactions simultaneously, using sparse data that vary substantially between countries and over time. A key element of the model is a two-dimensional second-order Intrinsic Gaussian Markov Random Field, which captures non-linear trends in the variables and their interactions. The model is fitted using Markov chain Monte Carlo methods, with a block Metropolis-Hastings algorithm providing efficient updates. Performance is demonstrated using simulated and real data.