Flexibly Accounting for Exposure Misclassification With External Validation Data.

ABSTRACT: 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 co

SUBMITTER: Edwards JK 

PROVIDER: S-EPMC7608057 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature