UC Berkeley

In order to develop more targeted, efficient, and effective psychotherapeutic interventions, calls have been made in the literature for greater use of idiographic hypothesis testing. Idiographic analyses can provide useful information regarding mechanisms of change within individuals over time during treatment. However, it remains unclear how clinicians might utilize idiographic statistical analyses during routine treatment to test clinical hypotheses, and in turn, guide treatment. We present an idiographic statistical framework for clinical hypothesis testing with routine treatment data that enables clinicians to examine a) whether the client's symptoms and hypothesized mechanisms change over time, b) whether trajectories of change reflect the timing of interventions, c) whether mechanisms predict subsequent symptoms, and d) whether relationships exist between multiple mechanisms, symptoms, or other treatment-related constructs over time. We demonstrate the utility of the approach for clinical hypothesis testing by applying it to routine treatment data collected from a 56 year-old male who presented with a combination of anger problems, anxiety, and depressive symptoms. We discuss how results from analyses can inform the case-formulation and guide clinical decision-making. We aim to make these methods more accessible by providing an online platform where clinicians can enter client data, test their clinical hypotheses using idiographic analyses, and utilize the results to disseminate their findings.