I have 2 questions about Multiple imputation (MI) in the assessment of the prognostic performance of a test. This test acts as a predictor of a specific outcome, 3 years in the future. I have 26 % of missing data in my outcome.
First, I performed the MI with a predictive matrix (an $N \times (n+1)$ matrix) that contains all the input variables in my database and the desired outcome of the predictor test. Here, $N$ is the number of observations and $n$ the number of input variables. I need to know whether this approach is sound. Can it be that multiple imputation needs to be performed in a $N \times n$ predictive matrix without the predictor outcome?
Second, after MI, the imputed data are obtained. How can I pool these data in one imputation data matrix? Can anyone share a script in R that performs this? Or are there other ways to analyze the imputed data, for this application?
Thanks in advance