jhodel posted on Thursday, October 03, 2019 - 6:39 am
Dear all, I would like to perform GMM on longitudinal data about persons undergoing clinical rehabilitation in order to explore the number of classes of recovery during rehabilitation. The study design includes max. 4 repeated unstructured measurement time points of an ordinal outcome (sum score between 0 and 100) which is non-normally distributed. Therefore I was thinking of fitting a skew-t GMM for categorical outcomes with individually-varying time scores. Is this possible in Mplus and do you have any examples on this?
I also read into the R package lcmm, which seem to be able to perform the desired GMM with unstructured data, non-normal and ordinal outcome by using Link Functions and Latent Process Mixed Models (https://arxiv.org/pdf/1503.00890.pdf). Unfortunately, there is very few literature around which describes the differences between the approaches used in the lcmm package and Mplus for skewed and categorical outcomes. Therefore, I was wondering if you know more about the differences and advantages/disadvantages of the two approaches used in these two softwares?