Evaluating robustness of a generalized linear model when applied to electronic health record data accessed using an Open API

Abstract

The Integrated Clinical and Environmental Exposures Service (ICEES) provides open regulatory-compliant access to clinical data, including electronic health record data, that have been integrated with environmental exposures data. While ICEES has been validated in the context of an asthma use case and several other use cases, the regulatory constraints on the ICEES open application programming interface (OpenAPI) result in data loss when using the service for multivariate analysis. In this study, we investigated the robustness of the ICEES OpenAPI through a comparative analysis, in which we applied a generalized linear model (GLM) to the OpenAPI data and the constraint-free source data to examine factors predictive of asthma exacerbations. Consistent with previous studies, we found that the main predictors identified by both analyses were sex, prednisone, race, obesity, and airborne particulate exposure. Comparison of GLM model fit revealed that data loss impacts model quality, but only with select interaction terms. We conclude that the ICEES OpenAPI supports multivariate analysis, albeit with potential data loss that users should be aware of.

Implications of rapid population growth on survey design and HIV estimates in the Rakai Community Cohort Study (RCCS), Uganda

“… 39 In the first step of 10-fold cross-validation, each Super Learner weighted seven candidate algorithms according to their prediction performance defined by the area under the receiver-operator-characteristic curve (AUC) (weights and descriptions of each algorithm are in the online supplemental table 4 ). Algorithms in R included gam, 40 biglasso, 41 xgboost, 42 glm and glm.interaction, 43 bayesglm 44 and ranger. 45 We used the default parameters in R for each candidate algorithm.…”