Using Time-Lagged Panel Data Analysis to Study Mechanisms of Change in Psychotherapy Research: Methodological Recommendations.

Abstract

The introduction of novel methodologies in the past decade has advanced research on mechanisms of change in observational studies. Time-lagged panel models allow to track session-by-session changes and focus on within-patient associations between predictors and outcomes. This shift is crucial, as change in mechanisms inherently takes place at a within-patient level. These models also enable preliminary casual inferences, which can guide the development of effective personalized interventions that target mechanisms of change, used at specific treatment phases for optimal effect. Given their complexity, panel models need to be implemented with caution, as different modeling choices can significantly affect results and reduce replicability. We outline three central methodological recommendations for use of time-lagged panel analysis to study mechanisms of change: a) Taking patient-specific effects into account, separating out stable between-person differences from within-person fluctuations over time; b) properly controlling for autoregressive effects; c) considering long-term time-trends. We demonstrate these recommendations in an applied example examining the session-by-session alliance-outcome association in a naturalistic psychotherapy study. We present limitations of time-lagged panel analysis and future directions.