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- Title
Large-scale evidence generation and evaluation across a network of databases (LEGEND): assessing validity using hypertension as a case study.
- Authors
Schuemie, Martijn J; Ryan, Patrick B; Pratt, Nicole; Chen, RuiJun; You, Seng Chan; Krumholz, Harlan M; Madigan, David; Hripcsak, George; Suchard, Marc A
- Abstract
<bold>Objectives: </bold>To demonstrate the application of the Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) principles described in our companion article to hypertension treatments and assess internal and external validity of the generated evidence.<bold>Materials and Methods: </bold>LEGEND defines a process for high-quality observational research based on 10 guiding principles. We demonstrate how this process, here implemented through large-scale propensity score modeling, negative and positive control questions, empirical calibration, and full transparency, can be applied to compare antihypertensive drug therapies. We assess internal validity through covariate balance, confidence-interval coverage, between-database heterogeneity, and transitivity of results. We assess external validity through comparison to direct meta-analyses of randomized controlled trials (RCTs).<bold>Results: </bold>From 21.6 million unique antihypertensive new users, we generate 6 076 775 effect size estimates for 699 872 research questions on 12 946 treatment comparisons. Through propensity score matching, we achieve balance on all baseline patient characteristics for 75% of estimates, observe 95.7% coverage in our effect-estimate 95% confidence intervals, find high between-database consistency, and achieve transitivity in 84.8% of triplet hypotheses. Compared with meta-analyses of RCTs, our results are consistent with 28 of 30 comparisons while providing narrower confidence intervals.<bold>Conclusion: </bold>We find that these LEGEND results show high internal validity and are congruent with meta-analyses of RCTs. For these reasons we believe that evidence generated by LEGEND is of high quality and can inform medical decision-making where evidence is currently lacking. Subsequent publications will explore the clinical interpretations of this evidence.
- Subjects
PROPENSITY score matching; CLINICAL trial registries; RANDOMIZED controlled trials; HYPERTENSION; LEGENDS; EVIDENCE; ANTIHYPERTENSIVE agents; DATABASES; RESEARCH; CONFIDENCE intervals; COMPUTER networks; CLINICAL trials; SCIENTIFIC observation; MEDICAL cooperation; TREATMENT effectiveness; COMPARATIVE studies; RESEARCH funding; PROBABILITY theory
- Publication
Journal of the American Medical Informatics Association, 2020, Vol 27, Issue 8, p1268
- ISSN
1067-5027
- Publication type
journal article
- DOI
10.1093/jamia/ocaa124