Depression studies frequently adopt two-stage designs to examine the efficacy of augmenting pharmacotherapy with psychotherapy. Initially subjects receive one of the several treatments; if respond, they continue the same treatment; however, if they fail to respond, they move to the next stage and are randomized to other treatment options. Outcome such as 24-item Hamilton Rating Scale of Depression (HRSD24) scores are then collected repeatedly to monitor the progress of the subject. The goal is to assess the effect of treatment regimes (consisting of initial treatment, initial response and the second stage treatment combinations) on HRSD24 profile. Statistical inference for assessing treatment regimes using a summary outcome measure such as mean response has been well-studied in the literature. However, statistical methods for assessing the effect of treatment regimes on repeated measures data are not well-developed. In this article, we propose two methods based on mixed models and multiple imputations to assess the effect of treatment regimes on the longitudinal HRSD24 scores. Methods are compared through simulation studies and through an application to a depression dataset.