|
Abstract:
|
While clinical trials remain a critical source for studying disease risk, progression and treatment response, they have limitations including the generalizability of the study findings to the real world and the limited ability to test broader hypotheses. In recent years, due to the increasing adoption of electronic health records (EHR) and the linkage of EHR with specimen bio-repositories, large integrated EHR datasets now exist as a new source for translational research. These datasets open new opportunities for deriving real-word, data-driven prediction models of disease risk and progression as well as unbiased investigation of shared genetic etiology of multiple phenotypes. Yet, they also bring methodological challenges. For example, obtaining validated treatment response information is a major bottleneck in conducting comparative effectiveness research with EHR data, as it requires laborious medical record review. In this talk, I'll discuss methods for making cause effects of treatments using EHR data in the presence of these challenges. These methods will be illustrated using EHR data from Partner's Healthcare.
|
