Guided Bayesian imputation to adjust for confounding when combining heterogeneous data sources in comparative effectiveness research
Year: 2017
Type: article
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 averaging, confounder selection, and missing data imputation into a single framework. It allows for different treatment effects in the main study and in the validation study, and propagates the uncertainty due to the missing data imputation and confounder selection when estimating the average causal effect (ACE) in the main study. We compare our method to several existing approaches via simulation. We apply our method to a study examining the effect of surgical resection on survival among 10 396 Medicare beneficiaries with a brain tumor when additional covariate information is available on 2220 patients in SEER-Medicare. We find that the estimated ACE decreases by 30% when incorporating additional information from SEER-Medicare.
Source: Biostatistics
Institution: Harvard University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
Awards: G1435456691, G3806058218, Cardiovascular Health and Air Pollution: A National Study, G4827808846, Casual Inference with Interference for Evaluating Air Quality Policies, G4869648682, G5101321364, Bayesian Methods for Comparative Effectiveness Research with Observational Data, Statistical Informatics for Cancer Research, G9000014778, Disparities in Exposure and Health Effects of Multiple Environmental Stressors Across the Life Course, Graduate Training in Biostatistics, Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer Research, G944279775
OpenAlex ID: W2593797352