Understanding an impact of patient enrollment pattern on predictability of central (unstratified) randomization in a multi-center clinical trial.

In a multi-center randomized controlled trial (RCT) with competitive recruitment, eligible patients are enrolled sequentially by different study centers and are randomized to treatment groups using the chosen randomization method. Given the stochastic nature of the recruitment process, some centers may enroll more patients than others, and in some instances, a center may enroll multiple patients in a row, for example, on a given day. If the study is open-label, the investigators might be able to make intelligent guesses on upcoming treatment assignments in the randomization sequence, even if the trial is centrally randomized and not stratified by center. In this paper, we use enrollment data inspired by a real multi-center RCT to quantify the susceptibility of two restricted randomization procedures, the permuted block design and the big stick design, to selection bias under the convergence strategy of Blackwell and Hodges (1957) applied at the center level. We provide simulation evidence that the expected proportion of correct guesses may be greater than 50% (i.e., an increased risk of selection bias) and depends on the chosen randomization method and the number of study patients recruited by a given center that takes consecutive positions on the central allocation schedule. We propose some strategies for ensuring stronger encryption of the randomization sequence to mitigate the risk of selection bias.

A better alternative to stratified permuted block design for subject randomization in clinical trials.

Stratified permuted block randomization has been the dominant covariate-adaptive randomization procedure in clinical trials for several decades. Its high probability of deterministic assignment and low capacity of covariate balancing have been well recognized. The popularity of this sub-optimal method is largely due to its simplicity in implementation and the lack of better alternatives. Proposed in this paper is a two-stage covariate-adaptive randomization procedure that uses the block urn design or the big stick design in stage one to restrict the treatment imbalance within each covariate stratum, and uses the biased-coin minimization method in stage two to control imbalances in the distribution of additional covariates that are not included in the stratification algorithm. Analytical and simulation results show that the new randomization procedure significantly reduces the probability of deterministic assignments, and improve the covariate balancing capacity when compared to the traditional stratified permuted block randomization.