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在针对时期进行平衡的交叉试验中进行盲法和非盲法样本量重新估计。

Blinded and unblinded sample size reestimation in crossover trials balanced for period.

作者信息

Grayling Michael J, Mander Adrian P, Wason James M S

机构信息

MRC Biostatistics Unit, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge, UK.

出版信息

Biom J. 2018 Sep;60(5):917-933. doi: 10.1002/bimj.201700092. Epub 2018 Aug 3.

DOI:10.1002/bimj.201700092
PMID:30073679
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6175184/
Abstract

The determination of the sample size required by a crossover trial typically depends on the specification of one or more variance components. Uncertainty about the value of these parameters at the design stage means that there is often a risk a trial may be under- or overpowered. For many study designs, this problem has been addressed by considering adaptive design methodology that allows for the re-estimation of the required sample size during a trial. Here, we propose and compare several approaches for this in multitreatment crossover trials. Specifically, regulators favor reestimation procedures to maintain the blinding of the treatment allocations. We therefore develop blinded estimators for the within and between person variances, following simple or block randomization. We demonstrate that, provided an equal number of patients are allocated to sequences that are balanced for period, the proposed estimators following block randomization are unbiased. We further provide a formula for the bias of the estimators following simple randomization. The performance of these procedures, along with that of an unblinded approach, is then examined utilizing three motivating examples, including one based on a recently completed four-treatment four-period crossover trial. Simulation results show that the performance of the proposed blinded procedures is in many cases similar to that of the unblinded approach, and thus they are an attractive alternative.

摘要

交叉试验所需样本量的确定通常取决于一个或多个方差分量的设定。在设计阶段,这些参数值的不确定性意味着试验往往存在效能不足或效能过高的风险。对于许多研究设计而言,通过考虑允许在试验期间重新估计所需样本量的适应性设计方法,这一问题已得到解决。在此,我们针对多治疗交叉试验提出并比较了几种实现此目的的方法。具体而言,监管机构倾向于采用重新估计程序以维持治疗分配的盲法。因此,我们针对个体内方差和个体间方差开发了遵循简单随机化或区组随机化的盲法估计量。我们证明,只要分配到在周期上平衡的序列中的患者数量相等,所提出的遵循区组随机化的估计量就是无偏的。我们还进一步给出了遵循简单随机化的估计量的偏差公式。然后利用三个实际例子,包括一个基于最近完成的四治疗四周期交叉试验的例子,检验了这些程序以及一种非盲法方法的性能。模拟结果表明,所提出的盲法程序在许多情况下的性能与非盲法方法相似,因此它们是一种有吸引力的替代方法。

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本文引用的文献

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Stat Med. 2016 Oct 15;35(23):4110-23. doi: 10.1002/sim.6975. Epub 2016 Apr 21.
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Pharm Stat. 2011 Jul-Aug;10(4):325-31. doi: 10.1002/pst.469. Epub 2010 Oct 28.
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