A Systematic Statistical Approach to Evaluating Evidence from Observational Studies

Author:

Madigan David12,Stang Paul E.23,Berlin Jesse A.4,Schuemie Martijn23,Overhage J. Marc25,Suchard Marc A.2678,Dumouchel Bill29,Hartzema Abraham G.210,Ryan Patrick B.23

Affiliation:

1. Department of Statistics, Columbia University, New York, New York 10027;

2. Observational Medical Outcomes Partnership, Foundation for the National Institutes of Health, Bethesda, Maryland 20810

3. Janssen Research and Development LLC, Titusville, New Jersey, 08560

4. Johnson & Johnson, New Brunswick, New Jersey, 08901;, , ,

5. Siemens Health Services, Malvern, Pennsylvania, 19355;

6. Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, California, 90095;

7. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California, 90095

8. Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, California, 90095

9. Oracle Health Sciences, Burlington, Massachusetts, 01803;

10. College of Pharmacy, University of Florida, Gainesville, Florida, 32610;

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.

Publisher

Annual Reviews

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

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