Bounding Formulas for Selection Bias
Author(s) -
Tzu-Hsuan Huang,
WenChung Lee
Publication year - 2015
Publication title -
american journal of epidemiology
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 2.33
H-Index - 256
eISSN - 1476-6256
pISSN - 0002-9262
DOI - 10.1093/aje/kwv130
Subject(s) - confounding , selection bias , bounding overwatch , information bias , observational study , selection (genetic algorithm) , econometrics , statistics , omitted variable bias , computer science , mathematics , artificial intelligence
Researchers conducting observational studies need to consider 3 types of biases: selection bias, information bias, and confounding bias. A whole arsenal of statistical tools can be used to deal with information and confounding biases. However, methods for addressing selection bias and unmeasured confounding are less developed. In this paper, we propose general bounding formulas for bias, including selection bias and unmeasured confounding. This should help researchers make more prudent interpretations of their (potentially biased) results.
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