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Activity Number: 558 - The Big Data Revolution in Health Care: Promise and Potential
Type: Topic Contributed
Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract #303042
Title: Reliable Healthcare Evidence from the Large-Scale Evidence Generation Across a Network of Databases (LEGEND) Study
Author(s): Marc Suchard*
Companies: UCLA
Keywords: Observational study; Negative controls; Reproducibility; Open Science
Abstract:

Concerns over reproducibility in science extend to research using existing healthcare data; many observational studies investigating the same topic produce conflicting results, even when using the same data. To address this problem, we propose a paradigm shift. The current paradigm centers on generating one estimate at a time using a unique study design with unknown reliability and publishing (or not) one estimate at a time. The new paradigm advocates for high-throughput observational studies using consistent and standardized methods, allowing evaluation, calibration, and unbiased dissemination to generate a more reliable and complete evidence base. We demonstrate this new paradigm by comparing all depression treatments for a set of outcomes, producing 17,718 hazard ratios, each using methodology on par with state-of-the-art studies. We include control hypotheses to evaluate and calibrate our evidence generation process. The distribution of effect size estimates reported in literature reveals an absence of small or null effects, with a sharp cutoff at p = 0.05. No such phenomena were observed in our results, suggesting more complete and more reliable evidence.


Authors who are presenting talks have a * after their name.

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