I have longitudinal data from N = 80 people who participated in 12 short monthly assessments. Around 40 participated in 10-12 of the interviews, the rest dropped out due to different reasons. Aim of the study is to assess the pattern of the course of symptoms during the 12 months.
I checked whether the missings are MCAR, MAR, or MNAR - as far as I can see from the dataset they are MAR.
To be able to calculate with the data, I would like to do multiple imputation with the missings (as far as I read this would be the optimal procedure). So far,
MICE was the package I was looking at. Doing this several questions arose:
Would multiple imputation be the right way to impute the missings? The persons have very different symptom courses (some decrease, others increase, others don't change over the 12 months) and I don't want the pattern of other persons to influence the pattern of a specific person (e.g. one with 2 time points missing). Is there a way that the pattern of other persons does not influence the imputation of a specific person?
So far I used the following formula
dataset_withoutmissings <- mice(dataset_withmissings) data_nomissings <- complete(dataset_withoutmissings, 1)
to impute the missings - I assume this is only the basic function and I am not quite sure whether I should add something important?