03: A Two-Stage Modeling Approach to Risk Prediction with High-Dimensional Two-Phase Data

Thomas M. Menino Convention & Exhibition Center 

When building risk prediction models using electronic health record (EHR) data, incorporating additional granular information from external sources may improve predictive accuracy. However, such external data is often available only for a subset of patients, resulting in monotone missingness. We formulate this problem within a two-phase design framework. In contrast to classical two-phase settings, the high dimensionality of EHR predictors and the complex availability mechanism of external data pose substantial challenges. To address these challenges, we propose a two-stage modeling method for building and evaluating risk prediction models for binary outcomes. In the first stage, we construct an initial prediction model using only EHR predictors under a sample-splitting procedure to summarize the high-dimensional predictors through a risk score. In the second stage, we fit a logistic regression model that recalibrates the initial model and incorporates the external variables as additional predictors. We develop a pseudo-score estimation approach that efficiently utilizes the EHR data and flexibly accounts for the differential availability of external data. We further propose a plug-in estimator for the area under the ROC curve. The proposed method is evaluated through theoretical characterization of its large sample properties and extensive simulation studies, and is further applied to develop a mortality risk prediction model for oncology patients using data from the University of Pennsylvania Health System EHRs enriched with additional patient survey information.

Electronic Health Records

High-dimensional risk prediction

Two-phase data

Two-stage Modeling 

Biometrics Section