Several different approaches have been applied to identify early positive change in response to psychotherapy so as to predict later treatment outcome and length as well as use this information for outcome monitoring and treatment planning. In this study, simple methods based on clinically significant change criteria and computationally demanding growth mixture modeling (GMM) are compared with regard to their overlap and uniqueness as well as their characteristics in terms of initial impairment, therapy outcome, and treatment length. The GMM approach identified a highly specific subgroup of early improving patients. These patients were characterized by higher average intake impairments and higher pre- to-posttreatment score differences. Although being more specific for the prediction of treatment success, GMM was much less sensitive than clinically significant and reliable change criteria. There were no differences between the groups with regard to treatment length. Because each of the approaches had specific advantages, results suggest a combination of both methods for practical use in routine outcome monitoring and treatment planning
