May 23, 2024 Webinar
A Semiparametric Method for Developing and Evaluating Risk Prediction Models using Integrated Electronic Health Record Data
Jill Hasler, PhD
Abstract:
When using electronic health records (EHRs) for clinical and translational research, additional data is often available from external sources to enrich the information extracted from EHRs. For example, academic biobanks have more granular data available, and patient reported data is often collected through small-scale surveys. It is common that the external data is available only for a small subset of patients who have EHR information. We propose efficient and robust methods for building and evaluating models for predicting the risk of binary outcomes using such integrated EHR data. Our method is built upon an idea derived from the two-phase design literature that modeling the availability of a patient's external data as a function of an EHR-based preliminary predictive score leads to effective utilization of the EHR data. Through both theoretical and simulation studies, we show that our method has high efficiency for estimating log-odds ratio parameters, the area under the ROC curve, as well as other measures for quantifying predictive accuracy. We apply our method to develop a model for predicting the short-term mortality risk of oncology patients, where the data was extracted from the University of Pennsylvania hospital system EHR and combined with survey-based patient reported outcome data.
Short Bio:
Dr. Jill Schnall Hasler is an Assistant Research Professor at Fox Chase Cancer Center. She works in the design and analysis of clinical, behavioral, and basic science research studies across the cancer care continuum. Her independent research focuses on the development of statistical methods for analyzing clinical data from Electronic Health Records, specifically focusing on EHR phenotyping and risk prediction.