CC BY-NC-ND 4.0 · Yearb Med Inform
DOI: 10.1055/s-0041-1726481
Research & Education

Drawing Reproducible Conclusions from Observational Clinical Data with OHDSI

George Hripcsak
1  Department of Biomedical Informatics, Columbia University, New York, New York, USA
2  Observational Health Data Sciences and Informatics, New York, New York, USA
,
Martijn J. Schuemie
2  Observational Health Data Sciences and Informatics, New York, New York, USA
3  Epidemiology Analytics, Janssen Research and Development, Titusville, New Jersey, USA
,
David Madigan
2  Observational Health Data Sciences and Informatics, New York, New York, USA
4  Northeastern University, Boston, Massachusetts, USA
,
Patrick B. Ryan
1  Department of Biomedical Informatics, Columbia University, New York, New York, USA
2  Observational Health Data Sciences and Informatics, New York, New York, USA
3  Epidemiology Analytics, Janssen Research and Development, Titusville, New Jersey, USA
,
Marc A. Suchard
2  Observational Health Data Sciences and Informatics, New York, New York, USA
5  Fielding School of Public Health, Department of Biostatistics, University of California, Los Angeles, Los Angeles, USA
6  David Geffen School of Medicine, Department of Biomathematics, University of California, Los Angeles, Los Angeles, USA
› Author Affiliations

Summary

Objective: The current observational research literature shows extensive publication bias and contradiction. The Observational Health Data Sciences and Informatics (OHDSI) initiative seeks to improve research reproducibility through open science.

Methods: OHDSI has created an international federated data source of electronic health records and administrative claims that covers nearly 10% of the world’s population. Using a common data model with a practical schema and extensive vocabulary mappings, data from around the world follow the identical format. OHDSI’s research methods emphasize reproducibility, with a large-scale approach to addressing confounding using propensity score adjustment with extensive diagnostics; negative and positive control hypotheses to test for residual systematic error; a variety of data sources to assess consistency and generalizability; a completely open approach including protocol, software, models, parameters, and raw results so that studies can be externally verified; and the study of many hypotheses in parallel so that the operating characteristics of the methods can be assessed.

Results: OHDSI has already produced findings in areas like hypertension treatment that are being incorporated into practice, and it has produced rigorous studies of COVID-19 that have aided government agencies in their treatment decisions, that have characterized the disease extensively, that have estimated the comparative effects of treatments, and that the predict likelihood of advancing to serious complications.

Conclusions: OHDSI practices open science and incorporates a series of methods to address reproducibility. It has produced important results in several areas, including hypertension therapy and COVID-19 research.



Publication History

Publication Date:
21 April 2021 (online)

© 2021. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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