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Maximum likelihood ratio tests for comparing the discriminatory ability of biomarkers subject to limit of detection.用于比较受检测限影响的生物标志物鉴别能力的最大似然比检验。
Biometrics. 2008 Sep;64(3):895-903. doi: 10.1111/j.1541-0420.2007.00941.x. Epub 2007 Nov 19.
2
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Stat Med. 2008 Jan 30;27(2):224-42. doi: 10.1002/sim.2760.
3
Comparing the areas under two correlated ROC curves: parametric and non-parametric approaches.比较两条相关ROC曲线下的面积：参数法和非参数法
Biom J. 2006 Aug;48(5):745-57. doi: 10.1002/bimj.200610223.
4
On linear combinations of biomarkers to improve diagnostic accuracy.关于生物标志物的线性组合以提高诊断准确性。
Stat Med. 2005 Jan 15;24(1):37-47. doi: 10.1002/sim.1922.
5
A new approach for interval estimation and hypothesis testing of a certain intraclass correlation coefficient: the generalized variable method.一种用于特定组内相关系数区间估计和假设检验的新方法：广义变量法。
Stat Med. 2004 Jul 15;23(13):2125-35. doi: 10.1002/sim.1782.
6
Combining diagnostic test results to increase accuracy.结合诊断测试结果以提高准确性。
Biostatistics. 2000 Jun;1(2):123-40. doi: 10.1093/biostatistics/1.2.123.
7
Inferences on the common mean of several normal populations based on the generalized variable method.基于广义变量法对多个正态总体共同均值的推断。
Biometrics. 2003 Jun;59(2):237-47. doi: 10.1111/1541-0420.00030.
8
Minimal and best linear combination of oxidative stress and antioxidant biomarkers to discriminate cardiovascular disease.用于区分心血管疾病的氧化应激和抗氧化生物标志物的最小及最佳线性组合。
Nutr Metab Cardiovasc Dis. 2002 Oct;12(5):259-66.
9
A non-parametric method for the comparison of partial areas under ROC curves and its application to large health care data sets.一种用于比较ROC曲线下部分面积的非参数方法及其在大型医疗数据集的应用
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10
Exact inference for growth curves with intraclass correlation structure.具有组内相关结构的生长曲线的精确推断。
Biometrics. 1999 Sep;55(3):921-4. doi: 10.1111/j.0006-341x.1999.00921.x.

基于联合生物标志物的配对曲线下面积（AUC）差异的置信区间估计

Confidence interval estimation of the difference between paired AUCs based on combined biomarkers.

作者信息

Tian Lili, Vexler Albert, Yan Li, Schisterman Enrique F

机构信息

Department of Biostatistics, University at Buffalo, 249 Farber Hall, 3435 Main St. Bldg. 26 Buffalo, NY 14214-3000, USA.

出版信息

J Stat Plan Inference. 2009;139(10):3725-3732. doi: 10.1016/j.jspi.2009.05.001.

DOI:10.1016/j.jspi.2009.05.001
PMID:19946609
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2782380/
Abstract

In many diagnostic studies, multiple diagnostic tests are performed on each subject or multiple disease markers are available. Commonly, the information should be combined to improve the diagnostic accuracy. We consider the problem of comparing the discriminatory abilities between two groups of biomarkers. Specifically, this article focuses on confidence interval estimation of the difference between paired AUCs based on optimally combined markers under the assumption of multivariate normality. Simulation studies demonstrate that the proposed generalized variable approach provides confidence intervals with satisfying coverage probabilities at finite sample sizes. The proposed method can also easily provide P-values for hypothesis testing. Application to analysis of a subset of data from a study on coronary heart disease illustrates the utility of the method in practice.

摘要

在许多诊断研究中，对每个受试者进行多项诊断测试或有多种疾病标志物可用。通常，应将这些信息结合起来以提高诊断准确性。我们考虑比较两组生物标志物之间鉴别能力的问题。具体而言，本文重点关注在多元正态性假设下基于最优组合标志物的配对AUC之间差异的置信区间估计。模拟研究表明，所提出的广义变量方法在有限样本量下提供了具有令人满意覆盖概率的置信区间。所提出的方法还可以轻松地为假设检验提供P值。对一项冠心病研究的部分数据进行分析的应用说明了该方法在实际中的效用。