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Latent class growth analysis is a popular approach to identify underlyingsubpopulations. Several implementations, such as LCGA (Mplus), Proc Traj (SAS)and lcmm (R) are specially designed for this purpose. Motivated by datacollection of psychological instruments over time in a large North Americancancer centre, we compare these implementations using various simulatedEdmonton Symptom Assessment System revised (ESAS-r) scores, an ordinal outcomefrom 0 to 10, as well as the real data consisting of more than 20,000 patients.We found that Mplus and lcmm lead to high correct classification rate, but ProcTraj over estimated the number of classes and failed to converge. While Mplusis computationally faster than lcmm, it does not allow more than 10 levels. Wetherefore suggest first analyzing data on the ordinal scale using lcmm. Ifcomputational time becomes an issue, then one can group the scores intocategories and implement them in Mplus.

Latent class growth analysis for ordinal response data in the Distress  Assessment and Response Tool: an evaluation of state-of-the-art  implementations