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A flexible, interpretable framework for assessing sensitivity to unmeasured confounding.

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第一作者： Vincent,Dorie

第一单位： Humanities & the Social Sciences, New York University, New York, NY, U.S.A.

作者： Vincent,Dorie ^[1] ; Masataka,Harada ^[2] ; Nicole Bohme,Carnegie ^[3] ; Jennifer,Hill ^[1]

作者单位： Humanities & the Social Sciences, New York University, New York, NY, U.S.A. ^[1] Economics, Fukuoka University, Fukuoka, Japan. ^[2] Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, U.S.A. ^[3]

关键词 Bayesian modeling causal inference nonparametric regression sensitivity analysis unmeasured confounding

医学主题词贝叶斯定理(Bayes Theorem);人类(Humans);营养调查(Nutrition Surveys)

DOI 10.1002/sim.6973

PMID 27139250

发布时间 2021-01-09

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Statistics in medicine

2016年35卷20期

3453-70页

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2016年35卷20期

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A flexible, interpretable framework for assessing sensitivity to unmeasured confounding.

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A flexible, interpretable framework for assessing sensitivity to unmeasured confounding.

Statistics in medicine

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Statistics in medicine

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