Modelling life course blood pressure trajectories using Bayesian adaptive splines

Abstract

No single study has collected data over individuals' entire lifespans. To understand changes over the entire life course, it is necessary to combine data from various studies that cover the whole life course. Such combination may be methodologically challenging due to potential differences in study protocols, information available and instruments used to measure the outcome of interest. Motivated by our interest in modelling blood pressure changes over the life course, we propose the use of Bayesian adaptive splines within a hierarchical setting to combine data from several UK-based longitudinal studies where blood pressure measures were taken in different stages of life. Our method allowed us to obtain a realistic estimate of the mean life course trajectory, quantify the variability both within and between studies, and examine overall and study specific effects of relevant risk factors on life course blood pressure changes.

Keywords: adaptive Bayesian splines; blood pressure; hierarchical models; repeated measurements; reversible jump Markov chain Monte Carlo; spline regression.

Figure 1.

Directed acyclic graph (DAG) corresponding to blood pressure life course model. ‘Nodes’ represent variables in the model and are joined together by arrows to show direct dependence between variables. Solid arrows denote stochastic dependence whereas ‘dashed’ arrows denote logical dependence (deterministic functions). Rectangular nodes represent covariates that have been included in the model. The rectangular ‘containers’ labelled $i=1,\dots ,I$, $j=1,\dots ,J_i$, etc. denote repetition, i.e. ‘loops’ over the index used in the label.

Figure 2.

Overall systolic blood pressure trajectories, with 95% credible bands, for males (dark grey) and females (light grey).