A Bayesian analysis of regression models with continuous errors with application to longitudinal studies.
We employ a regression model with errors that follow a continuous autoregressive process to analyse longitudinal studies. In this way, unequally spaced observations do not present a problem in the analysis. We employ a Bayesian approach, where our inferences are based on a direct resampling process that generates values from the posterior distribution of the parameters of the model. We illustrate these Bayesian inferences with an analysis of a longitudinal study that involves the regression of foetal head circumference on menstrual age. Using these same data, we contrast the Bayesian approach with a maximum likelihood technique.
Broemeling
L D
LD
University of Texas School of Public Health, El Paso 79968, USA.
Cook
P
P
D001499:Bayes Theorem; D002508:Cephalometry; D005333:Fetus / Q000033:anatomy & histology; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D015233:Models, Statistical; D012044:Regression Analysis; D018401:Sample Size