Accounting for uncertainty in confounder and effect modifier selection when estimating average causal effects in generalized linear models.

ABSTRACT: Confounder selection and adjustment are essential elements of assessing the causal effect of an exposure or treatment in observational studies. Building upon work by Wang et al. (2012, Biometrics 68, 661-671) and Lefebvre et al. (2014, Statistics in Medicine 33, 2797-2813), we propose and evaluate a Bayesian method to estimate average causal effects in studies with a large number of potential confounders, relatively few observations, likely interactions between confounders and the exposure of interest, and uncertainty on which confounders and interaction terms should be included. Our method is applicable across all exposures and outcomes that can be handled through generalized linear models. In this general setting, estimation of the average causal effect is different from estimation of th

SUBMITTER: Wang C 

PROVIDER: S-EPMC4575246 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature