A Bayesian statistical model to characterize blood pressure increase in salt‐sensitive hypertension in the rat (856.6)

In salt‐sensitive rat models of hypertension, characteristic patterns of increase in blood pressure (BP) are observed when salt intake in the diet is increased. In this study, we used a Bayesian statistical model to quantitate these patterns and draw conclusions about the differences between sets (strains or treatments) of rats. Time‐course BP data for individual rats was fit to a mixture of two cumulative normal distributions by Gibbs sampling, a process for approximating multivariate probability distributions. The mixture was defined by seven parameters, which describe aspects of the timing and extent of BP increase. Probability distributions for each parameter describe the frequency of a certain value over all Gibbs samples. Sets of rats were assessed on a single parameter level by taking parameter values for each rat as samples from a group parameter representing all the rats, which allowed us to assess differences between experimental sets. The model precisely fit all observed BP data, and is sufficient to detect significant differences in parameters between experimental sets. This Bayesian model provides a useful tool for distinguishing sets of rats based on the character of their BP increase. These insights provided by this analysis into the timing and extent of BP increase may provide a framework for the analysis of further data generated with these rats, and the mechanisms underlying these BP increases.