Leveraging External Validation Data: The Challenges of Transporting Measurement Error Parameters.

ABSTRACT: Approaches to address measurement error frequently rely on validation data to estimate measurement error parameters (e.g., sensitivity and specificity). Acquisition of validation data can be costly, thus secondary use of existing data for validation is attractive. To use these external validation data, however, we may need to address systematic differences between these data and the main study sample. Here, we derive estimators of the risk and the risk difference that leverage external validation data to account for outcome misclassification. If misclassification is differential with respect to covariates that themselves are differentially distributed in the validation and study samples, the misclassification parameters are not immediately transportable. We introduce two ways to account fo

SUBMITTER: Ross RK 

PROVIDER: S-EPMC10841744 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature