Multiple imputation with mice of twin datasets


I am struggling with imputation of covariates in a twin dataset. I am using co-twin design (generalised estimating equations) to estimate the within- and between-family effects. For the outcome and exposure, I am using complete cases. I would like to adjust the estimates for covariates that are twin-specific, but they have missing data.

How is the data imputation typically done for twin data? Is it possible to do it using mice package in R?

I have checked mice guidelines for imputation on multilevel data, but I am not sure I understand how to best adapted for twin data as the size (n) of each cluster/family is small.

Is it wrong to impute the twin data in wide format ignoring the clustering?

With many thanks and my best wishes,

Hi Sinziana

Usually, we use full information maximum likelihood so imputation is unnecessary. If the data are continuous, covariates can be regressed out as a preliminary step, but if they are ordinal or binary, it’s typically necessary to do the covariate adjustment in the model.


Hi Mike,

Thank you very much for your answer.

With my best wishes,