Large-scale evidence generation and evaluation across a network of databases (LEGEND): assessing validity using hypertension as a case study.,Journal of the American Medical Informatics Association

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Large-scale evidence generation and evaluation across a network of databases (LEGEND): assessing validity using hypertension as a case study.
Journal of the American Medical Informatics Association ( IF 6.4 ) Pub Date : 2020-09-10 , DOI: 10.1093/jamia/ocaa124
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, 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, 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, Connecticut, 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.

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.

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