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

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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Main Authors: 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
Format: Article
Language:English
Published: BMC 2024-11-01
Series:BMC Medical Research Methodology
Subjects:
Bayesian bias analysis
Inverse probability weighting
Missing not at random
Monte Carlo bias analysis
Multiple imputation
Probabilistic bias analysis
Online Access:https://doi.org/10.1186/s12874-024-02382-4
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https://doi.org/10.1186/s12874-024-02382-4

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