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用于分析具有高斯结果且受检测限影响的两组横断面数据的模型比较。

Comparison of models for analyzing two-group, cross-sectional data with a Gaussian outcome subject to a detection limit.

作者信息

Wiegand Ryan E, Rose Charles E, Karon John M

机构信息

Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, USA

Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, USA.

出版信息

Stat Methods Med Res. 2016 Dec;25(6):2733-2749. doi: 10.1177/0962280214531684. Epub 2014 May 5.

Abstract

A potential difficulty in the analysis of biomarker data occurs when data are subject to a detection limit. This detection limit is often defined as the point at which the true values cannot be measured reliably. Multiple, regression-type models designed to analyze such data exist. Studies have compared the bias among such models, but few have compared their statistical power. This simulation study provides a comparison of approaches for analyzing two-group, cross-sectional data with a Gaussian-distributed outcome by exploring statistical power and effect size confidence interval coverage of four models able to be implemented in standard software. We found using a Tobit model fit by maximum likelihood provides the best power and coverage. An example using human immunodeficiency virus type 1 ribonucleic acid data is used to illustrate the inferential differences in these models.

摘要

当生物标志物数据受到检测限时,在分析这些数据时就会出现一个潜在的困难。这个检测限通常被定义为真实值无法可靠测量的点。存在多种用于分析此类数据的回归型模型。已有研究比较了这些模型之间的偏差,但很少有研究比较它们的统计功效。这项模拟研究通过探索四种可在标准软件中实现的模型的统计功效和效应大小置信区间覆盖率,对分析两组横断面数据且结果呈高斯分布的方法进行了比较。我们发现,使用最大似然法拟合的 Tobit 模型具有最佳的功效和覆盖率。使用人类免疫缺陷病毒 1 型核糖核酸数据的一个例子来说明这些模型在推断上的差异。

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