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Guided Bayesian imputation to adjust for confounding when combining heterogeneous data sources in comparative effectiveness research.

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第一作者： Joseph,Antonelli

第一单位： Department of Biostatistics, Harvard TH Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115,USA.

作者： Joseph,Antonelli ^[1] ; Corwin,Zigler ^[1] ; Francesca,Dominici ^[1]

作者单位： Department of Biostatistics, Harvard TH Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115,USA. ^[1]

关键词 Bayesian adjustment for confounding Bayesian data augmentation Confounder selection Missing data Model averaging

医学主题词贝叶斯定理(Bayes Theorem);脑肿瘤(Brain Neoplasms);疗效比较研究(Comparative Effectiveness Research);人类(Humans);信息存储和检索(Information Storage and Retrieval);Medicare(Medicare);偏差(Uncertainty);美国(United States)

DOI 10.1093/biostatistics/kxx003

PMID 28334230

发布时间 2018-12-02

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Biostatistics (Oxford, England)

2017年18卷3期

553-568页

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Biostatistics (Oxford, England)

2017年18卷3期

553-568页

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Guided Bayesian imputation to adjust for confounding when combining heterogeneous data sources in comparative effectiveness research.

Biostatistics (Oxford, England)

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Guided Bayesian imputation to adjust for confounding when combining heterogeneous data sources in comparative effectiveness research.

Biostatistics (Oxford, England)

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Biostatistics (Oxford, England)

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