Imbalance properties of centre-stratified permuted-block and complete randomisation

What is it about?

Randomisation schemes are rules that assign patients to treatments in a clinical trial. Many of these schemes have the common aim of maintaining balance in the numbers of patients across treatment groups. The properties of imbalance that have been investigated in the literature are based on two treatment groups. In this paper, their properties for K > 2 treatments are studied for two randomisation schemes: centre-stratified permuted-block and complete randomisation. For both randomisation schemes, analytical approaches are investigated assuming that the patient recruitment process follows a Poisson–gamma model. When the number of centres involved in a trial is large, the imbalance for both schemes is approximated by a multivariate normal distribution. The accuracy of the approximations is assessed by simulation. A test for treatment differences is also considered for normal responses, and numerical values for its power are presented for centre-stratified permuted-block randomisation. To speed up the calculations, a combined analytical/approximate approach is used.

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Why is it important?

Randomisation schemes used in clinical trials are considered as essential and of great importance to maintain a balance in the numbers of patients across treatment groups and to gain some randomness in assigning a treatment to a patient to avoid any selection or accidental bias. Because of the stochastic nature of both patient enrolment and the randomisation process, imbalance in assignments can still occur, even in well-defined randomisation schemes. As a larger imbalance can reduce the power of a study, the statistical analysis of properties of imbalance is of paramount importance for clinical trial design.

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