Estimating the association between blood pressure variability and cardiovascular disease: An application using the ARIC Study

TL;DR

This paper investigates the bias in estimating blood pressure variability's effect on cardiovascular disease and proposes two advanced statistical methods to improve accuracy, demonstrated using ARIC study data.

Contribution

It introduces two novel methods, a two-stage approach and a joint model, to reduce bias in blood pressure variability estimation in relation to cardiovascular risk.

Findings

Both methods effectively reduce regression dilution bias.

Application to ARIC data shows improved hazard ratio estimates.

Joint modeling provides more accurate variability estimates.

Abstract

The association between visit-to-visit systolic blood pressure variability and cardiovascular events has recently received a lot of attention in the cardiovascular literature. But blood pressure variability is usually estimated on a person-by-person basis, and is therefore subject to considerable measurement error. We demonstrate that hazard ratios estimated using this approach are subject to bias due to regression dilution and we propose alternative methods to reduce this bias: a two-stage method and a joint model. For the two-stage method, in stage one repeated measurements are modelled using a mixed effects model with a random component on the residual standard deviation. The mixed effects model is used to estimate the blood pressure standard deviation for each individual, which in stage two is used as a covariate in a time-to-event model. For the joint model, the mixed effects sub-model and time-to-event sub-model are fitted simultaneously using shared random effects. We illustrate the methods using data from the Atherosclerosis Risk in Communities (ARIC) study.