Paired t-test evaluates mean change. Mixed models evaluate change in means. How the two can be compatible? For balanced data it works - paired t-test = random intercept model = GLS with compound symmetry. But the power in mixed models is they allow for missing data, say, one subject has 5 measurements, and the other - only 2. So the mean change won't be the same as change in means. So how the two methods can be advised for analysing repeated data?
Paired t-test gives mean change. Regression (also mixed) gives change in means. These usually aren't equal to each other. So the outcomes of these methods won't agree too. So either is wrong for the analysis. Which one?