Combining Internal and External Validation Data to Correct for Exposure Misclassification

Abstract

Internal validation data offer a well-recognized means to help correct for exposure misclassification or measurement error. When available, external validation data offer the advantage of cost-effectiveness. However, external data are a generally inefficient source of information about misclassification parameters. Furthermore, external data are not necessarily "transportable", for example, if there are differences in the design or target populations of the main and validation studies. Recent work has suggested weighted estimators to simultaneously take advantage of internal and external validation data. We explore efficiency and transportability in the fundamental case of estimating the odds ratio for binary exposure in a case-control setting. Our results support the use of closed-form weighted log odds ratio estimators in place of computationally demanding maximum likelihood estimators under both types of validation study designs (using internal data only, and combining internal and external data). We also provide and assess a formal test of the transportability assumption, and introduce a new log odds ratio estimator that is inherently robust to violation of that assumption. A case-control study of the association between maternal antibiotic use and sudden infant death syndrome provides a real-data example.