Model suggestion for zero-bounded dependent variable I have a repeated measures dataset (8 measurements) with three experimental groups (Randomly generated). The dependent variable (Y) is on a continuous scale bounded inferiorly at 0. I am trying to model the effect of the different groups and time on Y. What would be the appropriate model here? -

*

*repeated ANOVA is compromised by the non-normal distribution of Y
(see histogram -  I tried adding a minuscule amount and logging, but
still really to no help)

*Mixed linear models are not bounded inferiorly at 0. Perhaps one of the more advanced non-linear mixed models?

*Area under the curves for all timepoints stratified by group?

*Bayesian approaches?

Raw Y   
Logged Y
Histogram of logged Y


So I did fit a longitudinal bayesian proportional odds model using brlm as suggested by Frank Harrells. The fit is quite good and seems to model what I wanted while still retaining the inferior bounds. However, I would have liked to do a posterior predictive check, but I don't quite comprehend the calculation of the predicted probabilities given all these confusing intercepts and the other "standard" Bayesian toolboxes don't seem to recognize the brlm models. Moreover, I tried calculating the contrast between the different groups at t=2, but I am at odds as how I can convert the value into the raw Y-units. The calculated contrast between the green and red group at t=2 is 1.866.

