🤖 AI Summary
This study addresses the challenge in randomized controlled trials of complex interventions—such as psychotherapy—where conventional designs struggle to disentangle the intervention effect from therapist-specific effects. The authors introduce, for the first time, an orthogonal factorial design that treats the intervention (as a fixed effect) and therapist (as a random effect) as potentially interacting factors. Each therapist delivers all intervention conditions, and patients are randomly assigned to specific intervention–therapist combinations. Grounded in Design of Experiments (DoE) theory, the approach integrates ANOVA and regression modeling to establish a tailored randomization scheme and statistical analysis framework. Simulation results demonstrate that this method accurately estimates the main intervention effect, its standard error, between-therapist variance, and therapist-level heterogeneity in intervention effects, thereby substantially enhancing the reliability of evidence generated from complex interventions.
📝 Abstract
It is recognised that treatment-related clustering should be allowed for in the sample size and analyses of individually-randomised parallel-group trials that evaluate therapist-delivered interventions such as psychotherapy. Here, interventions are a treatment factor, but therapists are not. If the aim of a trial is to separate effects of therapists from those of interventions, we propose that interventions and therapists should be regarded as two potentially interacting treatment factors (one fixed, one random) with a factorial structure. We consider the specific design where each therapist delivers each intervention (crossed therapist-intervention design), and the resulting therapist-intervention combinations are randomised to patients. We adopt a classical Design of Experiments (DoE) approach to propose a family of orthogonal factorial designs and their associated data analyses, which allow for therapist learning and centre too. We set out the associated data analyses using ANOVA and regression and report the results of a small simulation study conducted to explore the performance of the proposed randomisation methods in estimating the intervention effect and its standard error, the between-therapist variance and the between-therapist variance in the intervention effect. We conclude that more purposeful trial design has the potential to lead to better evidence on a range of complex interventions and outline areas for further methodological research.