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Using electronic health record data to identify comparator populations for comparative effectiveness research

Published

October 2020

Citation

Ramsey SD, Adamson BJ, Wang X, Bargo D, Baxi SS, Ghosh S, Meropol NJ. Using electronic health record data to identify comparator populations for comparative effectiveness research. Journal of Medical Economics. 2020. https://doi.org/10.1080/13696998.2020.1840113

Our summary

EHR-derived RWD can enhance health technology appraisals by providing population-level evaluations of cancer therapies. This enables researchers to select comparator populations with high levels of clinical specificity. Because real world use of treatments does not always match practice guidelines or clinical trial inclusion criteria, choosing comparator populations involves a tradeoff between matching to replicate “optimized” care similar to a trial-defined population versus “realized” care that is more representative of real-world practice. This article delves into how to determine the appropriate population when conducting comparative effectiveness research.

Why this matters

Health Technology Appraisals (HTA) have to balance between considering analytically optimized populations to match the clinical data behind a new treatment, and considering populations that reflect the practical realities of the clinical setting of interest. By considering the strengths and limitations of an optimized and realized population, we establish how EHR data can be used to identify appropriate populations for indirect treatment comparisons when assessing the benefit of a new medicine. This is a big step in demonstrating how EHR-derived RWD can be used to support HTAs, specifically for single-arm trials.

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