Abstract
ObjectivesEpidemiologic 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 all three sources of bias simultaneously. Study Design and SettingWe 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 with more commonly used approaches that do not account for measurement error, missing values, or confounding. ResultsThe results demonstrate that the four approaches consistently outperform the alternative approaches on all performance metrics (bias, mean squared error, and confidence interval coverage). Even in simulated data of 100 subjects, these approaches perform well. ConclusionThere can be a large benefit of addressing measurement error, missing values, and confounding to improve the estimation of exposure–outcome relations, even when the available sample size is relatively small.
Highlights
Researchers in epidemiology often aim to make inferences about causal relations between an exposure and a health outcome while analyzing observational data that are incomplete, contain measurement error, and are subject to confounding, which are amongAuthors’ contributions: M.v.S. contributed to conceptualization, methodology, software, and writing the article
There can be a large benefit of addressing measurement error, missing values, and confounding to improve the estimation of exposureeoutcome relations, even when the available sample size is relatively small
The models that adjusted for confounding, incompleteness, and measurement error (MIME, FIML, MIRC, and Bayesian model (BAYES)) performed consistently better and were closer to the gold standard (GS) model than the approaches that did not account for all three sources of bias (CRUD, LADJ, and MIMD; Fig. 1)
Summary
Researchers in epidemiology often aim to make inferences about causal relations between an exposure and a health outcome while analyzing observational data that are incomplete, contain measurement error (and misclassifications), and are subject to confounding, which are among. Authors’ contributions: M.v.S. contributed to conceptualization, methodology, software, and writing the article. B.B.L.P.d.V. contributed to methodology, software, and reviewing and editing the article. L.N. contributed to methodology, software, validation, and reviewing and editing the article. R.H.H.G. contributed to conceptualization, funding acquisition, methodology, and writing and editing the article
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