Measurement error is common in epidemiology, but few studies use quantitative methods to account for bias due to mismeasurement. One potential barrier is that some intuitive approaches that readily combine with methods to account for other sources of bias, like multiple imputation for measurement error (MIME), rely on internal validation data, which are rarely available. Here, we present a reparameterized imputation approach for measurement error (RIME) that can be used with internal or external validation data. We illustrate the advantages of RIME over a naive approach that ignores measurement error and MIME using a hypothetical example and a series of simulation experiments. In both the example and simulations, we combine MIME and RIME with inverse probability weighting to account for confounding when estimating hazard ratios and counterfactual risk functions. MIME and RIME performed similarly when rich external validation data were available and the prevalence of exposure did not vary between the main study and the validation data. However, RIME outperformed MIME when validation data included only true and mismeasured versions of the exposure or when exposure prevalence differed between the data sources. RIME allows investigators to leverage external validation data to account for measurement error in a wide range of scenarios.
Keywords: causality; survival analysis; systematic bias.
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