Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA.
Department of Pharmacy, Uppsala University, Uppsala, Sweden.
BMC Med Res Methodol. 2024 Feb 28;24(1):52. doi: 10.1186/s12874-023-02131-z.
The design of a multi-center randomized controlled trial (RCT) involves multiple considerations, such as the choice of the sample size, the number of centers and their geographic location, the strategy for recruitment of study participants, amongst others. There are plenty of methods to sequentially randomize patients in a multi-center RCT, with or without considering stratification factors. The goal of this paper is to perform a systematic assessment of such randomization methods for a multi-center 1:1 RCT assuming a competitive policy for the patient recruitment process.
We considered a Poisson-gamma model for the patient recruitment process with a uniform distribution of center activation times. We investigated 16 randomization methods (4 unstratified, 4 region-stratified, 4 center-stratified, 3 dynamic balancing randomization (DBR), and a complete randomization design) to sequentially randomize patients. Statistical properties of the recruitment process and the randomization procedures were assessed using Monte Carlo simulations. The operating characteristics included time to complete recruitment, number of centers that recruited a given number of patients, several measures of treatment imbalance and estimation efficiency under a linear model for the response, the expected proportions of correct guesses under two different guessing strategies, and the expected proportion of deterministic assignments in the allocation sequence.
Maximum tolerated imbalance (MTI) randomization methods such as big stick design, Ehrenfest urn design, and block urn design result in a better balance-randomness tradeoff than the conventional permuted block design (PBD) with or without stratification. Unstratified randomization, region-stratified randomization, and center-stratified randomization provide control of imbalance at a chosen level (trial, region, or center) but may fail to achieve balance at the other two levels. By contrast, DBR does a very good job controlling imbalance at all 3 levels while maintaining the randomized nature of treatment allocation. Adding more centers into the study helps accelerate the recruitment process but at the expense of increasing the number of centers that recruit very few (or no) patients-which may increase center-level imbalances for center-stratified and DBR procedures. Increasing the block size or the MTI threshold(s) may help obtain designs with improved randomness-balance tradeoff.
The choice of a randomization method is an important component of planning a multi-center RCT. Dynamic balancing randomization with carefully chosen MTI thresholds could be a very good strategy for trials with the competitive policy for patient recruitment.
多中心随机对照试验（RCT）的设计涉及多个方面，如样本量的选择、中心的数量及其地理位置、研究参与者的招募策略等。有许多方法可以在多中心 RCT 中对患者进行序贯随机分组，包括有或没有分层因素的情况。本文的目的是对考虑到患者招募过程的竞争性策略的 1:1 多中心 RCT 中使用的这些随机分组方法进行系统评估。
我们考虑了一种带有均匀中心激活时间分布的泊松-伽马模型，用于描述患者招募过程。我们研究了 16 种随机分组方法（4 种非分层、4 种区域分层、4 种中心分层、3 种动态平衡随机分组（DBR）和完全随机分组设计），以对患者进行序贯随机分组。使用蒙特卡罗模拟评估了招募过程和随机分组程序的统计特性。研究的操作特征包括完成招募所需的时间、招募了给定数量患者的中心数量、线性模型下治疗不均衡的几种度量、响应估计效率、在两种不同猜测策略下的正确猜测的预期比例以及分配序列中确定性分配的预期比例。
最大耐受不均衡（MTI）随机分组方法，如大棒设计、Ehrenfest urn 设计和区块 urn 设计，比传统的区组随机化设计（PBD）具有更好的均衡-随机性权衡，无论是否分层。非分层随机分组、区域分层随机分组和中心分层随机分组可以控制所选水平（试验、区域或中心）的不均衡，但可能无法在其他两个水平上达到平衡。相比之下，DBR 在所有 3 个水平上都能很好地控制不均衡，同时保持治疗分配的随机性。增加研究中的中心数量有助于加速招募过程，但代价是增加了招募很少（或没有）患者的中心数量，这可能会增加中心分层和 DBR 程序的中心水平不均衡。增加区组大小或 MTI 阈值可以帮助获得更好的随机性-均衡权衡的设计。
随机分组方法的选择是多中心 RCT 计划的重要组成部分。对于具有患者招募竞争性策略的试验，选择精心挑选的 MTI 阈值的动态平衡随机分组可能是一种非常好的策略。
