Assessing the impact of managed-care on the distribution of length-of-stay using Bayesian hierarchical models.

Hierarchical models provide a useful framework for the complexities encountered in policy-relevant research in which the impact of social programs is being assessed. Such complexities include multi-site data, censored data and over-dispersion. In this paper, Bayesian inference through Markov Chain Monte Carlo methods is used for the analysis of a complex hierarchical log-normal model that shows the impact of a managed care strategy aimed at limiting length of hospital stays. Parameters in this model allow for variability in baseline length-of-stay as well as the program effect across hospitals. The authors demonstrate elicitation and sensitivity analysis with respect to prior distributions. All calculations for the posterior and predictive distributions were obtained using the software BUGS.