Linear mixed models were used to analyze data from clinical trials with repeated outcomes. The study focused on different assumptions about how treatment effects change over time. They found that depending on the model used, the estimated treatment effect could be negative even if all individual treatment effects were positive. This suggests that certain combinations of treatment effect models and covariance structures can lead to unexpected results in estimating treatment effects. The findings were demonstrated using data from a Parkinson's disease trial.