Model uncertainty and health effect studies for particulate matter

There are many aspects of model choice that are involved in health effect studies of particulate matter and other pollutants. Some of these choices concern which pollutants and confounding variables should be included in the model, what type of lag structure for the covariates should be used, which interactions need to be considered, and how to model nonlinear trends. Because of the large number of potential variables, model selection is often used to find a parsimonious model. Different model selection strategies may lead to very different models and conclusions for the same set of data. As variable selection may involve numerous test of hypotheses, the resulting significance levels may be called into question, and there is the concern that the positive associations are a result of multiple testing. Bayesian Model Averaging is an alternative that can be used to combine inferences from multiple models and incorporate model uncertainty. This paper presents objective prior distributions for Bayesian Model Averaging in generalized linear models so that Bayesian model selection corresponds to standard methods of model selection, such as the Akaike Information Criterion (AIC) or Bayes Information Criterion (BIC), and inferences within a model are based on standard maximum likelihood estimation. These methods allow non-Bayesians to describe the level of uncertainty due to model selection, and can be used to combine inferences by averaging over a wider class of models using readily available summary statistics from standard model fitting programs. Using Bayesian Model Averaging and objective prior distributions, we re-analyze data from Birmingham, AL and illustrate the role of model uncertainty in inferences about the effect of particulate matter on elderly mortality. Copyright (C) 2000 John Wiley and Sons, Ltd.

Duke Scholars

Author Merlise Clyde Statistical Science