Semi-supervised validation of multiple surrogate outcomes with application to electronic medical records phenotyping

The Electronic Medical Records (EMR) data linked with genomic data have facilitated efficient and large scale translational studies. One major challenge in using EMR for translational research is the difficulty in accurately and efficiently annotating disease phenotypes due to the low accuracy of billing codes and the time involved with manual chart review. Recent efforts such as those by the Electronic Medical Records and Genomics (eMERGE) Network and Informatics for Integrating Biology & the Bedside (i2b2) have led to an increasing number of algorithms available for classifying various disease phenotypes. Investigators can apply such algorithms to obtain predicted phenotypes for their specific EMR study. They typically perform a small validation study within their cohort to assess the algorithm performance and then subsequently treat the algorithm classification as the true phenotype for downstream genetic association analyses. Despite the superior performance compared to simple billing codes, these algorithms may not port well across institutions, leading to bias and low power for association studies. In this paper, we propose a semi-supervised method to make inferences about both the accuracy of multiple available algorithms and the effect of genetic markers on the true phenotype, leveraging information from both a large set of unlabeled data where both genetic markers and algorithm output information and a small validation data where labels are additionally available. The simulation studies show that the proposed method substantially outperforms existing methods from the missing data literature. The proposed methods are applied to an EMR study of how low density lipoprotein risk alleles affect the risk of cardiovascular disease among patients with rheumatoid arthritis.