Dynamic Models of Individual Change in Psychotherapy Process Research

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Abstract

Objective: There is a need for rigorous methods to study the mechanisms that lead to individual-level change (i.e., process-outcome research). We argue that panel data (i.e., longitudinal study of a number of individuals) methods have 3 major advantages for psychotherapy researchers: (1) enabling microanalytic study of psychotherapeutic processes in a clinically intuitive way, (2) modeling lagged associations over time to ensure direction of causality, and (3) isolating within-patient changes over time from between-patient differences, thereby protecting against confounding influences because of the effects of unobserved stable attributes of individuals. However, dynamic panel data methods present a complex set of analytical challenges. We focus on 2 particular issues: (1) how long-term trajectories in the variables of interest over the study period should be handled, and (2) how the use of a lagged dependent variable as a predictor in regression-based dynamic panel models induces endogeneity (i.e., violation of independence between predictor and model error term) that must be taken into account in order to appropriately isolate within- and between-person effects. Method: An example from a study of working alliance in psychotherapy in primary care in Sweden is used to illustrate some of these analytic decisions and their impact on parameter estimates. Results: Estimates were strongly influenced by the way linear trajectories were handled; that is, whether variables were “detrended” or not. Conclusions: The issue of when detrending should be done is discussed, and recommendations for research are provided.

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