Accounting for bias due to outcome data missing not at random: comparison and illustration of two approaches to probabilistic bias analysis: a simulation study

Abstract Background Bias from data missing not at random (MNAR) is a persistent concern in health-related research. A bias analysis quantitatively assesses how conclusions change under different assumptions about missingness using bias parameters that govern the magnitude and direction of the bias....

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書誌詳細

出版年:	BMC Medical Research Methodology
主要な著者:	Emily Kawabata, Daniel Major-Smith, Gemma L. Clayton, Chin Yang Shapland, Tim P. Morris, Alice R. Carter, Alba Fernández-Sanlés, Maria Carolina Borges, Kate Tilling, Gareth J. Griffith, Louise A. C. Millard, George Davey Smith, Deborah A. Lawlor, Rachael A. Hughes
フォーマット:	論文
言語:	英語
出版事項:	BMC 2024-11-01
主題:	Bayesian bias analysis Inverse probability weighting Missing not at random Monte Carlo bias analysis Multiple imputation Probabilistic bias analysis
オンライン･アクセス:	https://doi.org/10.1186/s12874-024-02382-4