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Multiple imputation of missing data under missing at random: including a collider as an auxiliary variable in the imputation model can induce bias

2023-06-18 epidemiology Title + abstract only
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Epidemiological studies often have missing data, which are commonly handled by multiple imputation (MI). In MI, in addition to those required for the substantive analysis, imputation models often include other variables ("auxiliary variables"). Auxiliary variables that predict the partially observed variables can reduce the standard error (SE) of the MI estimator and, if they also predict the probability that data are missing, reduce bias due to data being missing not at random. However, guidanc...

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