Using the E-Value to Assess the Potential Effect of Unmeasured Confounding in Observational Studies

JAMA. 2019 Feb 12;321(6):602-603. doi: 10.1001/jama.2018.21554.

Authors

Sebastien Haneuse¹, Tyler J VanderWeele^{1

2}, David Arterburn^{3

4}

Affiliations

¹ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
² Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
³ Kaiser Permanente Washington Health Research Institute, Seattle, Washington.
⁴ Department of Medicine, University of Washington, Seattle.

PMID: 30676631
DOI: 10.1001/jama.2018.21554

No abstract available

MeSH terms

Bariatric Surgery
Cerebrovascular Disorders
Confounding Factors, Epidemiologic*
Coronary Disease
Data Interpretation, Statistical*
Humans
Obesity, Morbid / complications
Obesity, Morbid / surgery
Observational Studies as Topic*
Proportional Hazards Models
Statistics as Topic / methods*