TITLE: Extracting Real-World Evidence from Real-World Data
SPEAKERS: Rebecca Hubbard, University of Pennsylvania and Xu Shi, University of Michigan
Interest in using real-world data (RWD), data generated outside of clinical trials, often as a by-product of digital transactions, has grown tremendously over the past decade and was further spurred by the passage of the 21st Century Cures Act. Two common sources of RWD, electronic health records (EHR) and medical claims, constitute a vast resource for the study of health conditions, interventions, and outcomes in the general population. Using RWD for research facilitates the efficient creation of large research databases, execution of pragmatic clinical trials, and study of rare diseases. Despite these advantages, there are many challenges for research conducted using RWD. To translate RWD into valid real-world evidence, statisticians must be aware of data generation, capture, and availability issues and utilize appropriate study designs and statistical analysis methods to account for these issues. This tutorial will introduce participants to the basic structure of EHR and claims data and discuss analytic approaches to working with these data through a combination of lecture and hands-on exercises in R. The first part of the course will cover issues related to the structure and quality of EHR and claims data, including data types and methods for extracting variables of interest; sources of missing data; error in covariates and outcomes extracted from RWD; and data capture considerations such as informative visit processes and medical records coding procedures. In the second half of the course, we will discuss statistical methods to mitigate some of the data quality issues arising in RWD, including missing data and error in covariates and outcomes. R code will be provided for implementation of the presented methods, and hands-on exercises will be used to compare results of alternative approaches.
Dr. Hubbard is a Professor of Biostatistics in the Department of Biostatistics, Epidemiology and Informatics at the University of Pennsylvania. Her research focuses on development and application of statistical methodology for studies using data from electronic health records (EHR). This work encompasses evaluation of screening and diagnostic tests, methods for comparative-effectiveness studies, and health services research. Dr. Hubbard’s methodological research emphasizes development of statistical tools to support valid inference for EHR-based analyses, accounting for complex data availability and data quality issues, and has been applied across a broad range of areas of application including oncology, neurology, and pharmacoepidemiology. Results of this work have been published in over 150 peer-reviewed papers in the statistical and medical literature. She has taught short courses at ENAR, the Summer Institutes in Statistical Genetics and Statistics for Clinical Research at the University of Washington, and for the ASA Council of Chapters for over 10 years.
Xu Shi is an Assistant Professor in the Department of Biostatistics at University of Michigan. Her research focuses on developing novel statistical methods that provide insights from high volume and high variability administrative healthcare data such as the electronic health records (EHR) data. She develops scalable and automated pipelines for curation and harmonization of EHR data across healthcare systems. She also develops causal inference methods that harness the full potential of EHR data to address comparative effectiveness and safety questions. She co-leads the Advanced Analytics Core of the FDA’s Sentinel Initiative Innovation Center to develop innovative statistical methods to monitor the safety of FDA-regulated medical products and explore novel ways to utilize information from distributed EHR data partners.