In this paper, a hierarchical generalized linear model with a Markov Chain Monte Carlo method is used to detect clustering effects for the disease rates. We first follow the criteria by Waller et al. (1997) and Gangnon and Clayton (2003) to assign prior distributions for the random effects in the model. With the Metropolis-Hasting algorithm, values of the random effects are simulated according to different selection methods of potential clusters. We then compute the Bayes factor and posterior distribution to decide the possible clusters for the diseases. This method is applied to analyze the data from National Health Research Institute. A simple comparison between the Bayes factor and posterior distribution for clustering detection is also made.
Date:
2008
Relation:
Journal of the Chinese Statistical Association. 2008;46(1):22-35.
