jamovi

Hi everybody,
I have a dataset of a parallel study where patients were assigned to treatment or placebo and evaluated with clinical tests at T0, T1 and T2 (one-month after treatment). The data seems to distribute in an inverted U-shape manner, meaning that there is an improvement at T1 that goes back to the baseline level at T2. However there is high data variability, because there are patients with different disease severity and phenotypes that have strikingly different scores at clinical tests. Moreover that sample size is low (about ten patients per group). How could I analyze this dataset? I've tried a linear mixed model with Time and Group as factors in order to account for patients heterogeneity, but I've read that there are also other methods, for example an ANCOVA with baseline score as covariate.. if you had suggestions they would be highly appreciated!
Thank you
Francesco

For me, 10 per group is too few for drawing confident conclusions, regardless of the type of analysis. But the more complicated the analysis, the less adequate 10-per-group would be. Repeated-measures ANOVA seems fine.