Commentary: practical advantages of Bayesian analysis of epidemiologic data

In the past decade, there have been enormous advances in the use of Bayesian methodology for analysis of epidemiologic data, and there are now many practical advantages to the Bayesian approach. Bayesian models can easily accommodate unobserved variables such as an individual’s true disease status in the presence of diagnostic error. The use of prior probability distributions represents a powerful mechanism for incorporating information from previous studies and for controlling confounding. Posterior probabilities can be used as easily interpretable alternatives to p values. Recent developments in Markov chain Monte Carlo methodology facilitate the implementation of Bayesian analyses of complex data sets containing missing observations and multidimensional outcomes.Tools are now available that allow epidemiologists to take advantage of this powerful approach to assessment of exposure-disease relations. Am J Epidemiol 2001;153:1222–6. Bayes theorem; epidemiologic methods; hierarchical Bayes; latent variable; Markov chain Monte Carlo; posterior probability; prior distribution Received for publication December 26, 2000, and accepted for publication March 12, 2001