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估计患病率对总体错误率的影响。

The effect of estimating prevalences on the population-wise error rate.

作者信息

Luschei Remi, Brannath Werner

机构信息

Institute for Statistics and Competence Center for Clinical Trials, University of Bremen, Bremen, Germany.

出版信息

Stat Methods Med Res. 2025 Feb;34(2):390-404. doi: 10.1177/09622802241307237. Epub 2025 Jan 19.

DOI:10.1177/09622802241307237
PMID:39828909
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11874581/
Abstract

The population-wise error rate is a type I error rate for clinical trials with multiple target populations. In such trials, a treatment is tested for its efficacy in each population. The population-wise error rate is defined as the probability that a randomly selected, future patient will be exposed to an inefficient treatment based on the study results. It can be understood and computed as an average of strata-specific family wise error rates and involves the prevalences of these strata. A major issue of this concept is that the prevalences are usually unknown in practice, so that the population-wise error rate cannot be directly controlled. Instead, one could use an estimator based on the given sample, like their maximum-likelihood estimator under a multinomial distribution. In this article, we demonstrate through simulations that this does not substantially inflate the true population-wise error rate. We differentiate between the expected population-wise error rate, which is almost perfectly controlled, and study-specific values of the population-wise error rate which are conditioned on all subgroup sample sizes and vary within a narrow range. Thereby, we consider up to eight different overlapping populations and moderate to large sample sizes. In these settings, we also consider the maximum strata-wise family wise error rate, which is found to be, on average, at least bounded by twice the significance level used for population-wise error rate control.

摘要

总体错误率是针对具有多个目标人群的临床试验的I型错误率。在这类试验中,会在每个群体中测试一种治疗方法的疗效。总体错误率定义为基于研究结果,随机选择的未来患者接受无效治疗的概率。它可以理解并计算为各层特定的族系错误率的平均值,并且涉及这些层的患病率。这个概念的一个主要问题是,在实际中患病率通常是未知的,因此总体错误率无法直接控制。相反,可以使用基于给定样本的估计量,比如多项分布下的最大似然估计量。在本文中,我们通过模拟证明这不会大幅提高实际的总体错误率。我们区分了几乎能得到完美控制的预期总体错误率,以及基于所有亚组样本量的特定研究的总体错误率值,这些值在一个狭窄范围内变化。因此,我们考虑了多达八个不同的重叠群体以及中等到大的样本量。在这些情况下,我们还考虑了最大层特定族系错误率,发现其平均至少受用于总体错误率控制的显著性水平的两倍限制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd05/11874581/94281591e346/10.1177_09622802241307237-fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd05/11874581/f14eb34be3ae/10.1177_09622802241307237-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd05/11874581/71e0a9ad5cb6/10.1177_09622802241307237-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd05/11874581/a904b7c21ac4/10.1177_09622802241307237-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd05/11874581/2a1d343a2d84/10.1177_09622802241307237-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd05/11874581/c99f5f7e715a/10.1177_09622802241307237-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd05/11874581/b986d0873303/10.1177_09622802241307237-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd05/11874581/94281591e346/10.1177_09622802241307237-fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd05/11874581/f14eb34be3ae/10.1177_09622802241307237-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd05/11874581/71e0a9ad5cb6/10.1177_09622802241307237-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd05/11874581/a904b7c21ac4/10.1177_09622802241307237-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd05/11874581/2a1d343a2d84/10.1177_09622802241307237-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd05/11874581/c99f5f7e715a/10.1177_09622802241307237-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd05/11874581/b986d0873303/10.1177_09622802241307237-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd05/11874581/94281591e346/10.1177_09622802241307237-fig7.jpg

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本文引用的文献

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伞式设计的效应量估计:使用随机或实用亚组分配处理多个生物标志物检测呈阳性的患者。
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