Approaches to Addressing Missing Values, Measurement Error and Confounding in Epidemiologic Studies.

Abstract Objective Epidemiologic studies often suffer from incomplete data, measurement error (or misclassification), and confounding. Each of these can cause bias and imprecision in estimates of exposure-outcome relations. We describe and compare statistical approaches that aim to control simultaneously all three sources of bias. Study Design and Setting We illustrate four statistical approaches that address all three sources of bias, namely, multiple imputation for missing data and measurement error, multiple imputation combined with regression calibration, full-information maximum likelihood within a structural equation modeling framework, and a Bayesian model. In a simulation study we assess the performance of the four approaches compared to more commonly used approaches that do not account for measurement error, missing values, or confounding. Results The results demonstrate that the four approaches consistently outperform the alternative approaches on all performance metrics (bias, mean squared error, confidence interval coverage). Even in simulated data of 100 subjects, these approaches perform well. Conclusion There can be a large benefit of addressing measurement error, missing values, and confounding to improve estimation of exposure-outcome relations, even when the available sample size is relatively small.