期刊
AMERICAN JOURNAL OF EPIDEMIOLOGY
卷 184, 期 5, 页码 354-356出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/aje/kww063
关键词
confounding; epidemiologic methods; influenza vaccine; selection bias; test-negative study design
资金
- NIAID NIH HHS [R01 AI085073] Funding Source: Medline
- NICHD NIH HHS [DP2 HD084070] Funding Source: Medline
In this issue of the Journal, Sullivan et al. (Am J Epidemiol. 2016; 184(5):345-353) carefully examine the theoretical justification for use of the test-negative design, a common observational study design, in assessing the effectiveness of influenza vaccination. Using modern causal inference methods (in particular, directed acyclic graphs), they describe different threats to the validity of inferences drawn about the effect of vaccination from test-negative design studies. These threats include confounding, selection bias, and measurement error in either the exposure or the outcome. While confounding and measurement error are common in observational studies, the potential for selection bias inherent in the test-negative design brings into question the validity of inferences drawn from such studies.
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