Orthogonal Factorial Designs for Trials of Therapist‐Delivered Interventions: Randomizing Intervention‐Therapist Combinations to Patients
Year: 2026
Type: article
Abstract: It is recognized that treatment-related clustering should be allowed for in the sample size and analyses of individually-randomized parallel-arm 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 and 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 randomized 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 center 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 randomization 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.
Source: Statistics in Medicine
DOI: 10.1002/sim.70813
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,450
Awards: Developing Statistical Methods for Empirically Optimising Complex Interventions: Applying ‘Design of Experiments’ Methods in Health and Social Care, Trial design and analysis methods to incorporate the complexities of surgery - Evaluating the individual and combined effects of the constituent components of surgical interventions and the effects of surgical learning curves., Feasibility Study: A Mathematical Language for Complex Healthcare Interventions
OpenAlex ID: W7125680553