Analysis of ambulatory blood pressure monitor data using a hierarchical model incorporating restricted cubic splines and heterogeneous within-subject variances.

Hypertensive disorders of pregnancy are associated with significant maternal and foetal morbidity. Measurement of blood pressure remains the standard way of identifying individuals at risk. There is growing interest in the use of ambulatory blood pressure monitors (ABPM), which can record an individual's blood pressure many times over a 24-hour period. From a clinical perspective interest lies in the shape of the blood pressure profile over a 24-hour period and any differences in the profile between groups. We propose a two-level hierarchical linear model incorporating all ABPM data into a single model. We contrast a classical approach with a Bayesian approach using the results of a study of 206 pregnant women who were asked to wear an ABPM for 24 hours after referral to an obstetric day unit with high blood pressure. As the main interest lies in the shape of the profile, we use restricted cubic splines to model the mean profiles. The use of restricted cubic splines provides a flexible way to model the mean profiles and to make comparisons between groups. From examining the data and the fit of the model it is apparent that there were heterogeneous within-subject variances in that some women tend to have more variable blood pressure than others. Within the Bayesian framework it is relatively easy to incorporate a random effect to model the between-subject variation in the within-subject variances. Although there is substantial heterogeneity in the within-subject variances, allowing for this in the model has surprisingly little impact on the estimates of the mean profiles or their confidence/credible intervals. We thus demonstrate a powerful method for analysis of ABPM data and also demonstrate how heterogeneous within-subject variances can be modelled from a Bayesian perspective. Copyright 2001 John Wiley & Sons, Ltd.