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.

翻译：在评估心理治疗等治疗师实施干预的个体随机平行组试验中，已认识到应在样本量计算和数据分析中考虑治疗相关的聚类效应。在此类试验中，干预是处理因素，而治疗师则不是。若试验目的是区分治疗师效应与干预效应，我们建议将干预和治疗师视为两个可能交互的处理因素（一个固定，一个随机），并采用析因结构。我们考虑特定设计方案：每位治疗师实施每种干预（交叉的治疗师-干预设计），并将由此产生的治疗师-干预组合随机分配给患者。我们采用经典实验设计方法，提出一系列正交析因设计及其关联的数据分析方案，该方案同时考虑了治疗师学习效应和中心效应。我们通过方差分析和回归分析阐述了相应的数据分析方法，并报告了一项小型模拟研究的结果，该研究旨在探讨所提随机化方法在估计干预效应及其标准误、治疗师间方差以及干预效应中治疗师间方差方面的表现。我们的结论是：更有针对性的试验设计有望为一系列复杂干预措施提供更优质的证据，并指出了需要进一步开展方法学研究的领域。