Multiple imputation using auxiliary imputation variables that only predict missingness can increase bias due to data missing not at random

Abstract Background Epidemiological and clinical studies often have missing data, frequently analysed using multiple imputation (MI). In general, MI estimates will be biased if data are missing not at random (MNAR). Bias due to data MNAR can be reduced by including other variables (“auxiliary variab...

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Principais autores:	Elinor Curnow, Rosie P. Cornish, Jon E. Heron, James R. Carpenter, Kate Tilling
Formato:	Artigo
Idioma:	Inglês
Publicado:	BMC 2024-10-01
Series:	BMC Medical Research Methodology
Assuntos:	Missing data Multiple imputation Bias amplification Auxiliary variable ALSPAC
Acceso en liña:	https://doi.org/10.1186/s12874-024-02353-9
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