Guided Bayesian imputation to adjust for confounding when combining heterogeneous data sources in comparative effectiveness research.

ABSTRACT: In comparative effectiveness research, we are often interested in the estimation of an average causal effect from large observational data (the main study). Often this data does not measure all the necessary confounders. In many occasions, an extensive set of additional covariates is measured for a smaller and non-representative population (the validation study). In this setting, standard approaches for missing data imputation might not be adequate due to the large number of missing covariates in the main data relative to the smaller sample size of the validation data. We propose a Bayesian approach to estimate the average causal effect in the main study that borrows information from the validation study to improve confounding adjustment. Our approach combines ideas of Bayesian model avera

SUBMITTER: Antonelli J 

PROVIDER: S-EPMC5862356 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature