Fredrik Falkenström1, Nili Solomonov2, Julian A Rubel3. 1. Department of Behavioral Sciences and Learning, Linköping University, Linköping, Sweden. 2. Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medical College, White Plains, NY, USA. 3. Department of Psychology, Justus Liebig University Giessen, Giessen, Germany.
Abstract
Objective: Existing evidence highlights the importance of modeling differential therapist effectiveness when studying psychotherapy outcome. However, no study to date examined whether this assertion applies to the study of within-patient effects in mechanisms of change. The study investigated whether therapist effects should be modeled when studying mechanisms of change on a within-patient level. Methods: We conducted a Monte Carlo simulation study, varying patient- and therapist level sample sizes, degree of therapist-level nesting (intra-class correlation), balanced vs. unbalanced assignment of patients to therapists, and fixed vs random within-patient coefficients. We estimated all models using longitudinal multilevel and structural equation models that ignored (2-level model) or modeled therapist effects (3-level model). Results: Across all conditions, 2-level models performed equally or were superior to 3-level models. Within-patient coefficients were unbiased in both 2- and 3-level models. In 3-level models, standard errors were biased when number of therapists was small, and this bias increased in unbalanced designs. Ignoring random slopes led to biased standard errors when slope variance was large; but 2-level models still outperformed 3-level models. Conclusions: In contrast to treatment outcome research, when studying mechanisms of change on a within-patient level, modeling therapist effects may even reduce model performance and increase bias.
Objective: Existing evidence highlights the importance of modeling differential therapist effectiveness when studying psychotherapy outcome. However, no study to date examined whether this assertion applies to the study of within-patient effects in mechanisms of change. The study investigated whether therapist effects should be modeled when studying mechanisms of change on a within-patient level. Methods: We conducted a Monte Carlo simulation study, varying patient- and therapist level sample sizes, degree of therapist-level nesting (intra-class correlation), balanced vs. unbalanced assignment of patients to therapists, and fixed vs random within-patient coefficients. We estimated all models using longitudinal multilevel and structural equation models that ignored (2-level model) or modeled therapist effects (3-level model). Results: Across all conditions, 2-level models performed equally or were superior to 3-level models. Within-patient coefficients were unbiased in both 2- and 3-level models. In 3-level models, standard errors were biased when number of therapists was small, and this bias increased in unbalanced designs. Ignoring random slopes led to biased standard errors when slope variance was large; but 2-level models still outperformed 3-level models. Conclusions: In contrast to treatment outcome research, when studying mechanisms of change on a within-patient level, modeling therapist effects may even reduce model performance and increase bias.