Disentangling therapist from intervention effects has long been considered a challenge in randomised trials of therapist delivered interventions. When separating these effects is of interest, factorial designs have been proposed in which therapists and interventions are regarded as two potentially interacting components of a complex intervention, represented by two treatment factors (one random, one fixed). In these trial designs, every therapist delivers each intervention, and patients are randomised to the resulting therapist intervention combinations. It is assumed that each therapist is allocated the same number of patients (equal capacity), that all therapists in a centre are available when a patient is randomised (equal availability), and that therapists remain involved for the entire duration of the trial (no turnover). In practice, these are strong assumptions that may lead to delayed or suspended randomisation and increased trial costs. We propose three novel randomisation strategies that explicitly account for therapist capacity, availability, and turnover while maintaining desirable statistical properties. In a simulation study, we compared the proposed and existing randomisation strategies across trial scenarios and design parameters, assessing trade offs between operational efficiency and statistical performance. The results provide practical guidance for trialists designing trials of therapist delivered interventions under real world constraints.

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