All Times EDT
Interest in conducting research using real-world data, data generated as a by-product of digital transactions, has exploded over the past decade and has been further spurred by the 21st Century Cures Act. Real-world data facilitate understanding of treatment utilization and outcomes as they occur in routine practice, and studies using these data sources can potentially proceed rapidly compared to trials and observational studies that rely on primary data collection. However, using data sources that were not collected for research purposes comes at a cost, and naïve use of such data without considering their complexity and imperfect quality can lead to bias and inferential error. Real-world data frequently violate the assumptions of standard statistical methods, but it is not practicable to develop new methods to address every possible complication arising in their analysis. The statistician is faced with a quandary: how to effectively utilize real-world data to advance research without compromising best practices for principled data analysis. In this talk I will use examples from my research on methods for the analysis of electronic health records (EHR) derived-data to illustrate approaches to understanding the data generating mechanism for real-world data. Drawing on this understanding, I will then discuss approaches to identify, use, and develop principled methods for incorporating EHR into research. The overarching goal of this presentation is to raise awareness of challenges associated with the analysis of real-world data and demonstrate how a principled approach can be grounded in an understanding of the scientific context and data generating process.