Combining Observational Studies to Reduce Multiple Biases

Epidemiology stands to benefit greatly from combining data sources with complementary strengths. We propose a study design and estimators to combine information from multiple observational studies to simultaneously address confounding and outcome measurement error. Using inverse probability weighted, g-computation, and augmented inverse probability weighted estimators, we show how to combine information from two studies wherein the first study is subject to outcome misclassification (but has adequate confounder control) and the second study has gold-standard outcomes (but inadequate confounder control). Monte Carlo experiments demonstrate that the proposed estimators remove both confounding and measurement biases and provide appropriate 95% confidence interval coverage, while standard analyses fall short. Fusion designs offer a principled approach to combine data from multiple sources to address multiple biases in epidemiologic research.