Many late-phase clinical trials recruit subjects at multiple study sites. This introduces a hierarchical structure into the data that can result in a power-loss compared to a more homogeneous single-center trial. Building on a recently proposed approach to sample size determination, we suggest a sample size recalculation procedure for multicenter trials with continuous endpoints. The procedure estimates nuisance parameters at interim from noncomparative data and recalculates the sample size required based on these estimates. In contrast to other sample size calculation methods for multicenter trials, our approach assumes a mixed effects model and does not rely on balanced data within centers. It is therefore advantageous, especially for sample size recalculation at interim. We illustrate the proposed methodology by a study evaluating a diabetes management system. Monte Carlo simulations are carried out to evaluate operation characteristics of the sample size recalculation procedure using comparative as well as noncomparative data, assessing their dependence on parameters such as between-center heterogeneity, residual variance of observations, treatment effect size and number of centers. We compare two different estimators for between-center heterogeneity, an unadjusted and a bias-adjusted estimator, both based on quadratic forms. The type 1 error probability as well as statistical power are close to their nominal levels for all parameter combinations considered in our simulation study for the proposed unadjusted estimator, whereas the adjusted estimator exhibits some type 1 error rate inflation. Overall, the sample size recalculation procedure can be recommended to mitigate risks arising from misspecified nuisance parameters at the planning stage.

中文翻译：

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基于非比较数据的多中心随机对照临床试验样本量重新计算

许多后期临床试验在多个研究地点招募受试者。这在数据中引入了分层结构，与更同质的单中心试验相比，这可能导致功率损失。基于最近提出的样本量确定方法，我们建议对具有连续终点的多中心试验进行样本量重新计算程序。该程序根据非比较数据估计中期的干扰参数，并根据这些估计重新计算所需的样本量。与多中心试验的其他样本量计算方法相比，我们的方法假设混合效应模型并且不依赖于中心内的平衡数据。因此，它是有利的，特别是对于临时重新计算样本量。我们通过一项评估糖尿病管理系统的研究来说明所提出的方法。进行蒙特卡罗模拟以使用比较和非比较数据评估样本量重新计算程序的操作特征，评估它们对参数的依赖性，例如中心间异质性、观察的剩余方差、处理效果大小和中心数量。我们比较了两种不同的中心间异质性估计量，一个未调整的估计量和一个偏置调整后的估计量，两者都基于二次形式。对于我们在模拟研究中考虑的所有参数组合，1 类错误概率和统计功效接近于它们对提议的未调整估计量的标称水平，而调整后的估计量表现出某种 1 类错误率膨胀。总体，