Modeling Change Trajectories for Mental Health Symptoms and Functioning During Psychotherapy

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Abstract

Psychological symptoms are routinely measured in clinic settings using self-report surveys to help researchers understand the nature of client progress. Past studies have generally used metrics that compare client scores at two time points (beginning and end of treatment) to classify progress by whether there has been significant improvement or deterioration in their symptom levels. However, contemporary practice often uses more frequent (e.g., weekly) assessment. Thus, methodologies incorporating data from every assessment, such as multilevel modeling, are used to provide more nuanced information about change trajectories. Though there is research on the uses of both methodological frameworks, little research has examined how these two methods can be used in conjunction with one another. In this study, I used secondary data to investigate if and how these two analytic methods can be used to complement one another. Deidentified data from 42 clients at a clinical psychology doctoral training clinic in Virginia were used to evaluate the study question. Assessment measures included the Brief Adjustment Scale (BASE-6), Generalized Anxiety Disorder Scale (GAD-7), and the Patient Health Questionnaire (PHQ-9). For each measure, RCI metrics and clinical significance thresholds were obtained from existing research and clients were grouped according to their pre-to-post treatment RCI and whether they had passed the clinical significance threshold during treatment. Multilevel models were constructed to describe change trajectories for each of these groups. From these models, descriptive and visual output was produced providing a foundation by which to compare results for each group of clients. This study will provide information concerning the nature of client progress across different analytic methods, and will advance a framework for future research in this area of study.

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