Dear Stata users,

I am trying to perform longitudinal analysis for the first time, so would appreciate any advise.
I have a longitudinal dataset on cancer patients with data collected at initial enrollment and 7 follow-up surveys. Some patients died during the course of study so not all patients have data available for 8 time points. All patients have data for at least 2 time points.
My dependent variable is an ordered variable ranging from 1 to 6. Independent variables include time varying variables such as age, quality of life measure and anxiety score that were collected at each time point, and time-invariant variables (collected only at initial enrollment) such as gender and ethnicity.
I want to investigate the patient trajectories for change in within-patient dependent variable overtime and the predictors for such change.
Ideally, I would want to see if I can find homogeneous sub-group of patients that have a common trajectory (latent class trajectory analysis).

I have come across the following common approaches to deal with longitudinal data analysis:
1. Mixed effects ordered logistic regression with ID and time random effects
Initial literature suggests performing mixed effects ordered logistic regression with ID and time random effects, however, it is noted that mixed effects shows estimates of population averaged trajectory (fixed effects) and ID random effect will give me the variation in individual trajectories around this average.

2. Latent class growth trajectory analysis
Each parameter would vary in each class/group of patients that have distinct trajectory shape.
I came across "traj" package to run such analysis, however, only normal, zero-inflated normal, zero-inflated poisson and binary logit model can be run using this syntax.
I read that LatentGold can be used to perform such analysis on ordinal variables, but I am trying to see if Stata can be used for such analysis.

Question: Please advise what would be the most appropriate analysis to be performed such that individual level variation can be explored overtime.

Apologies in advance if my question seems unclear, and thank you for your time and guidance.

Regards,
Isha