Johannes, Krisam, Yevgen, Ryeznik, Kerstine, Carter, Olga, Kuznetsova and Sverdlov, Alex (2024) Understanding an impact of patient enrollment pattern on predictability of central randomization in a multi-center clinical trial. Statistics in medicine. ISSN 1097-0258; 0277-6715

Abstract

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, e.g., 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 the enrollment data from 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., increased risk of the 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.

Item Type:	Article
Date Deposited:	09 Jul 2024 00:46
Last Modified:	09 Jul 2024 00:46
URI:	https://oak.novartis.com/id/eprint/52853