NO UNMEASURED CONFOUNDING: KNOWN UNKNOWNS OR… NOT?
作者信息
Schulz Juliana, Moodie Erica E M, Shortreed Susan M
出版信息
Am J Epidemiol. 2023 Sep 1;192(9):1604-1605. doi: 10.1093/aje/kwad133.
Abstract
摘要
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Sensitivity analyses informed by tests for bias in observational studies.基于观察性研究偏倚检验的敏感性分析。
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