Objective: Recently, innovative statistical tools have been used to model patterns of change in psychological treatments. These tools can detect patterns of change in patient progress early in treatment and allow for the prediction of treatment outcomes and treatment length.
Method: We used growth mixture modeling to identify different latent classes of early change in patients with panic disorder (N = 326) who underwent a manualized cognitive-behavioral treatment.
Results: Four latent subgroups were identified, showing clusters of change trajectories over the first 5 sessions. One of the subgroups consisted of patients whose symptoms rapidly decreased and also showed the best outcomes. This information improved treatment prediction by 16.1% over patient intake characteristics. Early change patterns also significantly predicted patients' early treatment termination. Patient intake characteristics that significantly predicted class membership included functional impairment and separation anxiety.
Conclusions: These findings suggest that early treatment changes are uniquely predictive of treatment outcome.