Zeitreihenmodelle von Wirkfaktoren der Psychotherapie


Tschacher W, Baur N, Grawe K. Temporal Interaction of Process Variables in Psychotherapy. Psychotherapy Research, 2000; 296-309.
Abstract: A sample of courses of dyadic psychotherapy using different treatment modalities was analyzed in order to study session-by-session dynamics. The sample was made up of 91 patients (mean age 33 yrs). The process data consisted of therapist and patient session reports and therapy outcome was evaluated by pre-post questionnaires and direct measures of change. After data reduction by principal component analysis, linear time series models of the resulting factors were computed to describe the prototypical dynamical patterns of the sample and of the modality subsamples (cognitive-behavioral, client-centered, schema-theoretical psychotherapy). It was found that the factor of Patient's Sense of Self-Efficacy/Morale governed the observed dynamics of the sample, whereas the therapeutic bond factors did have less impact on the dynamics. The dynamical patterns of client-centered therapies differed from other modalities. The dynamics-outcome findings showed that direct measures of change were associated with a specific process pattern in which the patient's sense of self-efficacy was supported by other process variables.

Tschacher W & Ramseyer F (2009). Modeling Psychotherapy Process by Time-Series Panel Analysis (TSPA). Psychotherapy Research, 19, 469–481.
Abstract: Wir führen in eine Methode der aggregierten Zeitreihenanalyse ein (Zeitreihen-Panelanalyse, engl. Time-Series Panel Analysis, TSPA), mit deren Hilfe prototypische Muster aus longitudinalen Therapieprozessdaten modelliert werden können. Diese Methode wird anhand eines Datensatzes dargestellt, der die Trajektorien von 202 Patienten in ambulanter Psychotherapie (zwischen 15 und 107 Sitzungen pro Patient) enthält. Mittels Vorstundenbögen wurden vor jeder Sitzung die Variablen 'subjektives Wohlbefinden' und 'Therapiemotivation' von den Patienten eingeschätzt. Wir verglichen an diesen Daten TSPA mit der Analyse von Wachstumskurven. Bei beiden Methoden wurden feste Effekte geschätzt, und die longitudinalen Daten wurden unbalanciert sowie unter Berücksichtigung mehrerer Ebenen analysiert. In die TSPA geht die Information zeitverschobener Zusammenhänge ein (sog. Granger-Kausalität), womit wir zeigen konnten, dass zwischen Wohlbefinden und Therapiemotivation Rückkopplungsbeziehungen bestehen. Die Wachstumskurvenanalyse belegte logarithmische Zunahmen der Trajektorien von Wohlbefinden. Besonders TSPA erscheint geeignet, Veränderungsprozesse im Therapieprozess zu beleuchten, da auch nichtexperimentelle Daten als kausale dynamische Strukturen analysiert werden können. Schlagworte Änderungsmechanismen; Granger-Kausalität; longitudinale Daten; Psychotherapieprozess; subjektives Wohlbefinden; Therapiemotivation; Trajektorien; Wachstumskurvenanalyse; Zeitreihen-Panelanalyse (TSPA)

Ramseyer F, Kupper Z, Caspar F, Znoj H, & Tschacher W (2014). Time-Series Panel Analysis (TSPA) – Multivariate modeling of temporal associations in psychotherapy process. Journal of Consulting and Clinical Psychology
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 explicitlyfocusing 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.

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