Model averaging in the analysis of leukemia mortality among Japanese A-bomb survivors

Abstract

Epidemiological studies often include numerous covariates, with a variety of possible approaches to control for confounding of the association of primary interest, as well as a variety of possible models for the exposure–response association of interest. Walsh and Kaiser (Radiat Environ Biophys 50:21–35, 2011) advocate a weighted averaging of the models, where the weights are a function of overall model goodness of fit and degrees of freedom. They apply this method to analyses of radiation–leukemia mortality associations among Japanese A-bomb survivors. We caution against such an approach, noting that the proposed model averaging approach prioritizes the inclusion of covariates that are strong predictors of the outcome, but which may be irrelevant as confounders of the association of interest, and penalizes adjustment for covariates that are confounders of the association of interest, but may contribute little to overall model goodness of fit. We offer a simple illustration of how this approach can lead to biased results. The proposed model averaging approach may also be suboptimal as way to handle competing model forms for an exposure–response association of interest, given adjustment for the same set of confounders; alternative approaches, such as hierarchical regression, may provide a more useful way to stabilize risk estimates in this setting.