CE_06C: Clinical evidence generation using electronic health records data (Added Fee)

This full day short course will introduce participants to the basic structure of EHR data and analytic approaches to working with these data through a combination of lectures and hands-on exercises in R. The first part of the course will cover issues related to the structure and quality of EHR data, including data types, record generation process, methods for extracting variables of interest, and potential sources of bias such as confounding bias. Participants will explore synthetic EHR-derived data sets to gain familiarity with the structure of EHR data and data exploration and visualization tools for identifying data quality issues. In the second half of the course, we will discuss statistical methods to mitigate data quality issues arising in EHR, including confounding, error in EHR-derived covariates and outcomes, and data integration across multiple clinical practices. R code will be provided for implementation of the presented methods, and hands-on exercises will be used to compare results of alternative approaches. This short course is of interest to researchers without prior experience working with EHR data as well as more experienced individuals interested in learning practical solutions to some common analytic challenges. The overarching objective of this course is to provide participants with an introduction to the structure and content of EHR data as well as a set of practical tools to investigate and analyze this rich data resource. About the presenters Xu Shi is an Assistant Professor in the Department of Biostatistics at the University of Michigan. She is interested in developing statistical methods for administrative healthcare databases such as EHR and claims data, focusing on EHR data harmonization across healthcare systems and comparative effectiveness and safety research. She co-leads the Causal Inference Core of the FDA's Sentinel Initiative to develop innovative statistical methods to monitor the safety of FDA-regulated medical products and explore novel ways to utilize information from distributed data partners. She has developed a semester-long course on the analysis of EHR data that has been highly successful at the University of Michigan. She has also taught several short courses that cover similar topics at the Deming Conference on Applied Statistics, the New England Statistics Symposium, the VERITY 2021 Bioinformatics Course on Bioinformatics in Clinical Data, and the Big Data Summer Institute at the University of Michigan. Yong Chen is a Professor of Biostatistics at the University of Pennsylvania. He has a strong interest in statistical theory with a focus on robust inference, and methodological research on leveraging large healthcare data (EHR data, administrative claims data) for evidence-based medicine and personalized disease prevention/intervention strategies. He is keen in developing informatics and statistical methods with associated software, using EHR data, to facilitate evidence extraction and synthesis for comparative effectiveness studies, as well as well-calibrated risk prediction models for aiding clinical decision-making. He has published over 180 peer-reviewed papers in statistical inference, medical informatics, comparative effectiveness research, and biomedical sciences. He has taught short courses at FDA, JSM, ENAR, ASA Biopharmaceutical Section, the Deming Conference, the New England Statistics Symposium, ICSA annual conference, and workshops at the UPenn in the last 10 years.