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I am running several melogit commands whereby I am accounting for clustering of individuals (it is a repeated measures design). So, I have melogit commands that look like this (increasing complexity).

Code:

melogit suicide score if flw_up == 1|| ppt_id:, or

Code:

 melogit suicide score age i.sex_ i.employment i.ethnicity if flw_ == 1 || ppt_id:, or

I have a total of 800 participants who have flw_up==1, but the number of groups in the output varies. For the first line of code the output indicates a total of 800 groups and for the second one 799. I thought that melogit uses all available information and does not carry out listwise deletion as with other models, is this not the case? Am I missing something?

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In general, mixed effects models handle missingness on the outcome variable but not on the predictors. With missing predictor data on a time-varying predictor, only the row of data corresponding to that time point will be excluded. Other rows of data for that participant are included in the estimation routine. And any outcome missigness is treated as missing at random (missingness is random conditional on covariates in the model). However, if a participant is missing on a time-invariant (or participant level variable) such as ethnicity or sex, then all observations for that participant will be listwise deleted.

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