Principles of Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND).,Journal of the American Medical Informatics Association

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Principles of Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND).
Journal of the American Medical Informatics Association ( IF 4.7 ) Pub Date : 2020-09-10 , DOI: 10.1093/jamia/ocaa103
Martijn J Schuemie _{1,

2} , Patrick B Ryan _{1,

3} , Nicole Pratt ₄ , RuiJun Chen _{3,

5} , Seng Chan You ₆ , Harlan M Krumholz ₇ , David Madigan ₈ , George Hripcsak _{3,

9} , Marc A Suchard _{2,

10}

Affiliation

Epidemiology Analytics, Janssen Research and Development, Titusville, New Jersey, USA.
Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, California, USA.
Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, USA.
Quality Use of Medicines and Pharmacy Research Centre, University of South Australia, Adelaide, South Australia, Australia.
Department of Medicine, Weill Cornell Medical College, New York, New York, USA.
Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea.
Department of Medicine, Yale University School of Medicine, New Haven, California, USA.
Department of Statistics, Columbia University, New York, New York, USA.
Medical Informatics Services, New York-Presbyterian Hospital, New York, New York, USA.
Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, California, USA.

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.

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