Principles of Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND).

ABSTRACT: Evidence derived from existing health-care data, such as administrative claims and electronic health records, can fill evidence gaps in medicine. However, many claim such data cannot be used to estimate causal treatment effects because of the potential for observational study bias; for example, due to residual confounding. Other concerns include P hacking and publication bias. In response, the Observational Health Data Sciences and Informatics international collaborative launched the Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) research initiative. Its mission is to generate evidence on the effects of medical interventions using observational health-care databases while addressing the aforementioned concerns by following a recently proposed paradigm

SUBMITTER: Schuemie MJ 

PROVIDER: S-EPMC7481029 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature