Time-series panel analysis (TSPA): Multivariate modeling of temporal associations in psychotherapy process.

Abstract

Objective: Processes occurring in the course of psychotherapy are characterized by the simple fact that they unfold in time and that the multiple factors engaged in change processes vary highly between individuals (idiographic phenomena). Previous research, however, has neglected the temporal perspective by its traditional focus on static phenomena, which were mainly assessed at the group level (nomothetic phenomena). To support a temporal approach, the authors introduce Time Series Panel Analysis (TSPA), a statistical methodology explicitly focusing on the quantification of temporal, session-to-session, aspects of change in psychotherapy. TSPA-models are initially built at the level of individuals, and are subsequently aggregated at the group level, thus allowing the exploration of prototypical models. Method:TSPA is based on vector autoregression (VAR), an extension of univariate autoregression models to multivariate time-series data. The application of TSPA is demonstrated in a sample of 87 outpatient psychotherapy patients that were monitored by post-session questionnaires.Prototypical mechanisms of change were derived from the aggregation of individual multivariate models of psychotherapy process. In a second step, the associations between mechanisms of change (TSPA) and pre-to-post symptom-change were explored. Results: TSPA allowed identifying a prototypical process pattern, where patient's alliance and self-efficacy were linked by a temporal feedback-loop. Furthermore, therapist's stability over time in both mastery-and clarification interventions was positively associated with better outcome. Conclusions: TSPA is a statistical tool that sheds new light on temporal mechanisms of change. Through this approach, clinicians may gain insight into prototypical patterns of change in psychotherapy. When asked to describe the mechanisms of therapeutic change in a patient, a psychotherapist will likely point to associations between certain factors of the therapist-patient system. These associations occur temporally and in sequence: "When I did X, the patient responded Y". A therapist relies on highly idiographic information, temporal evolution, and the action of multiple factors when evaluating therapy effectiveness. Yet, comparing these properties of therapists' reasoning to the strategies predominant in psychotherapy research (such as outcome research, process research), a stark contrast exists between a therapist's real-life complexity and research-dictated simplicity. This paper aims to show that the viewpoints and interests of the practitioner and the scientist need not be mutually exclusive, and that time-series (i.e. repeated measurements) may capture the dynamics of a psychotherapy process and vastly broaden the analytic and inferential possibilities. We present statistical methodology that accounts for temporal and individual-level information (idiographic perspective) and also generates predictions at a general or group level (nomothetic perspective). In the following application of Time Series P...

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