A Systematic Statistical Approach to Evaluating Evidence from Observational Studies

Posted: 7 Mar 2014

See all articles by David Madigan

David Madigan

Columbia University - Department of Statistics

Paul E. Stang

Foundation for the National Institutes of Health - Observational Medical Outcomes Partnership

Jesse A. Berlin

Johnson & Johnson

Martijn Schuemie

Johnson & Johnson

J. Marc Overhage

Foundation for the National Institutes of Health - Observational Medical Outcomes Partnership

Marc A. Suchard

University of California, Los Angeles (UCLA) - Department of Biostatistics

Bill Dumouchel

Oracle Health Sciences

Abraham Hartzema

University of Florida - College of Pharmacy

Patrick Ryan

Foundation for the National Institutes of Health - Observational Medical Outcomes Partnership

Date Written: January 2014

Abstract

Threats to the validity of observational studies on the effects of interventions raise questions about the appropriate role of such studies in decision making. Nonetheless, scholarly journals in fields such as medicine, education, and the social sciences feature many such studies, often with limited exploration of these threats, and the lay press is rife with news stories based on these studies. Consumers of these studies rely on the expertise of the study authors to conduct appropriate analyses, and on the thoroughness of the scientific peer-review process to check the validity, but the introspective and ad hoc nature of the design of these analyses appears to elude any meaningful objective assessment of their performance. Here, we review some of the challenges encountered in observational studies and review an alternative, data-driven approach to observational study design, execution, and analysis. Although much work remains, we believe this research direction shows promise.

Suggested Citation

Madigan, David and Stang, Paul E. and Berlin, Jesse A. and Schuemie, Martijn and Overhage, J. Marc and Suchard, Marc A. and Dumouchel, Bill and Hartzema, Abraham and Ryan, Patrick, A Systematic Statistical Approach to Evaluating Evidence from Observational Studies (January 2014). Annual Review of Statistics and Its Application, Vol. 1, Issue 1, pp. 11-39, 2014, Available at SSRN: https://ssrn.com/abstract=2405895 or http://dx.doi.org/10.1146/annurev-statistics-022513-115645

David Madigan (Contact Author)

Columbia University - Department of Statistics ( email )

Mail Code 4403
New York, NY 10027
United States

Paul E. Stang

Foundation for the National Institutes of Health - Observational Medical Outcomes Partnership ( email )

Bethesda, MD 20810
United States

Jesse A. Berlin

Johnson & Johnson ( email )

New Brunswick, NJ 08933
United States

Martijn Schuemie

Johnson & Johnson ( email )

New Brunswick, NJ 08933
United States

J. Marc Overhage

Foundation for the National Institutes of Health - Observational Medical Outcomes Partnership ( email )

Bethesda, MD 20810
United States

Marc A. Suchard

University of California, Los Angeles (UCLA) - Department of Biostatistics ( email )

Los Angeles, CA 90095
United States

Bill Dumouchel

Oracle Health Sciences ( email )

Burlington, MA 01803
United States

Abraham Hartzema

University of Florida - College of Pharmacy ( email )

United States

Patrick Ryan

Foundation for the National Institutes of Health - Observational Medical Outcomes Partnership ( email )

Bethesda, MD 20810
United States

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