Hello there,
I am imputing 17 candidate predictors, as well as 14 non-predictor variables in which model calibration will be assessed across. 9 of the 14 non-predictor variables are just alternate derivations of the candidate predictors (e.g. categorized continuous predictor) or variables used to derive the candidate predictors.
Using the mice package, I imputed all these aforementioned target variables using an imputation model which had the following as predictors: the 17 candidate predictors, the study outcome, and the survey cycle (as 2 candidate predictors not asked in 2 of the cycles).
I realized I didn’t include the 14 non-predictor variables as predictors in the imputation model, despite them being target variables. How big of an issue is this if these 14 variables are not involved in the actual modelling but rather just a small part of model validation? After all, I don’t believe these 14 variables inform missingness in the 17 candidate predictors, especially if 9 of the 14 variables are just alternate derivations of the candidate predictors or variables used to derive the candidate predictors.
Would appreciate any feedback!
Thanks,
Raf