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?
Multiple imputation is an appropriate approach for your situation but you need to account for the multilevel nature of your data. The observations are nested within participants and this fact needs to be considered when making the imputations. So you will need to select a multilevel imputation method. MICE offers several such methods and they all begin with 2L dot (e.g., 2l.norm). You can read more about the approach in this article: "Multilevel multiple imputation: A review and evaluation of joint modeling and chained equations imputation" http://doi.org/10.1037/met0000063 and you can read more about implementing it in MICE in this article: "Multivariate imputation by chained equations" http://doc.utwente.nl/78938/1/Buuren11mice.pdf