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I have a longitudinal database that has more than 50% of the missing data of the MAR type.

This amount of missing values was a surprise to me because I did not foresee this in the study design, and for that reason I cannot delete those that have missing values because my sample N will be very small. however, these imputation methods are new to me, and I am facing many difficulties.

I saw some tutorials on multiple imputation through the MICE package. So the following points were not clear:

How to specify categorical variables (The categorical variables have no missing data). In my case I have two categorical variables, one with two levels (before and after) and the other with 4 levels (grp1,grp2, grp3, grp4). and eight continuous variables with missing values (D0 to D7).

Just like in the example below:

enter image description here

When imputing data I would like to specify that an imputation should occur through categorical variables (time and group).

I used the code below but I don't know if considered the categorical variables

 library(mice)
 imputed_Data <- mice (my.data, m = 5, maxit = 50, method = 'pmm', seed = 500)
 

After making the imputations, I would like to change from the wide format to the long format, where there would be only the columns: ID, Name, Time, group and a two columns one with the repeated mensure (D0 to D6) and other with Values. I know how to do this with DAtaFrames, but the object generated by mice is of the mids type. As in the example below:

![enter image description here

As my data are repeated (dependent) measures, I want to perform the analysis of generalized equation estimation.

fit<- with(geeglm(value ~ Time+ group,
              data= IM.imputed_Data ,family=gaussian,id=ID,
              corstr="ar1"))


  summary(pool(fit))
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You could use tidyr::gather() to transform your data into long format and then use the package jomo for multiple imputation accounting for the nested structure of the data.

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  • $\begingroup$ I tried to use ´gather´but the object generated by mice is of the mids type, and gather did not work. $\endgroup$ – Kledson Lemes Mar 29 at 13:50
  • $\begingroup$ Sorry, I meant using gather() first. Once you have the data in long format, then proceed to imputing. $\endgroup$ – teamug Mar 29 at 18:01
  • $\begingroup$ teamug I thought that to use mice the variables should be in columns (Wide format) .. I will try it like this. $\endgroup$ – Kledson Lemes Mar 29 at 19:13
  • $\begingroup$ That is probably true, but if you want to proceed in long format, jomo can handle it, and specifying the cluster is pretty straightforward. $\endgroup$ – teamug Mar 30 at 5:56
  • $\begingroup$ Okay, the jomo package is new to me so I research it to understand it better. I don't know if I will have enough time, but I will try. $\endgroup$ – Kledson Lemes Mar 30 at 13:33
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Have you tried the following:

fit<- with(imputed_Data, geeglm(value ~ Time+ group,
              family=gaussian,id=ID,
              corstr="ar1"))


summary(pool(fit))
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    $\begingroup$ ,I did a test with other data that I have and it worked, but with my data it is not working because I couldn't change from wide format to long $\endgroup$ – Kledson Lemes Mar 29 at 17:06

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