# Sensitivity analysis using R's mice package with multiple missing variables

I am using mice to multiply impute data on a dataset with many variables with missing values. I followed this vignette to do a sensitivity analysis to understand how the imputations are influenced by violations of the MAR assumption in one missing variable. However, I am using mice to impute more than one missing variable. This vignette uses the delta adjustment technique. I am currently using all variables (including the missing variables) that are included in final analysis as predictors in the predictor matrix to impute the dataset.

My question: Is it valid to adjust more than one variable at a time when imputing the dataset (ie, instead of setting the varying delta levels for one variable as described in the vignette, set the delta, eg. 10% of the mean of the raw data, for several variables at a time, and then impute the data)? Or do I have to perform sensitivity analysis for each imputed variable one at a time?

Following the vignette, and setting the delta for more than one variable at a time, one could come up with this approach:

  # perform a dry run
ini <- mice(data, maxit = 0)
# obtain post processing matrix
post <- ini$post # create the delta vector for each variable # set delta to 0, +10% and +20% of the raw mean value delta <- list() for (var in vars$missing) {
d1 <- mean(data[[var]],na.rm=T) * 0.1
d2 <- mean(data[[var]],na.rm=T) * 0.2
delta[[var]] <- c(0, d1, d2)
}

# impute the data set by changing the imputed values
# using mice's post-processing capability
imp.all <- vector("list", length(delta))
for (i in 1:length(delta[])){
for (var in vars\$missing) {
d <- delta[[var]][i]
cmd <- paste("imp[[j]][,i] <- imp[[j]][,i] +", d)
post[[var]] <- cmd
}
imp <- mice::mice(data, post = post, maxit = 5, seed = 1, print = FALSE)
imp.all[[i]] <- imp
}