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本文引用的文献

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2
Regularized ROC method for disease classification and biomarker selection with microarray data.用于基于微阵列数据的疾病分类和生物标志物选择的正则化ROC方法。
Bioinformatics. 2005 Dec 15;21(24):4356-62. doi: 10.1093/bioinformatics/bti724. Epub 2005 Oct 18.
3
Estimation of time-dependent area under the ROC curve for long-term risk prediction.用于长期风险预测的ROC曲线下随时间变化面积的估计。
Stat Med. 2006 Oct 30;25(20):3474-86. doi: 10.1002/sim.2299.
4
The sensitivity and specificity of markers for event times.事件发生时间标志物的敏感性和特异性。
Biostatistics. 2006 Apr;7(2):182-97. doi: 10.1093/biostatistics/kxi047. Epub 2005 Aug 3.
5
Survival model predictive accuracy and ROC curves.生存模型预测准确性和ROC曲线。
Biometrics. 2005 Mar;61(1):92-105. doi: 10.1111/j.0006-341X.2005.030814.x.
6
Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation.生存分析中作为区分度度量的总体C:特定模型的总体值及置信区间估计
Stat Med. 2004 Jul 15;23(13):2109-23. doi: 10.1002/sim.1802.
7
Intrapartum and neonatal single-dose nevirapine compared with zidovudine for prevention of mother-to-child transmission of HIV-1 in Kampala, Uganda: 18-month follow-up of the HIVNET 012 randomised trial.在乌干达坎帕拉,与齐多夫定相比,分娩期及新生儿单剂量奈韦拉平预防HIV-1母婴传播的效果:HIVNET 012随机试验的18个月随访
Lancet. 2003 Sep 13;362(9387):859-68. doi: 10.1016/S0140-6736(03)14341-3.
8
Time-dependent ROC curves for censored survival data and a diagnostic marker.删失生存数据和诊断标志物的时间依赖性ROC曲线。
Biometrics. 2000 Jun;56(2):337-44. doi: 10.1111/j.0006-341x.2000.00337.x.
9
Predictive accuracy and explained variation in Cox regression.Cox回归中的预测准确性和解释变异
Biometrics. 2000 Mar;56(1):249-55. doi: 10.1111/j.0006-341x.2000.00249.x.
10
Assessment and comparison of prognostic classification schemes for survival data.生存数据预后分类方案的评估与比较
Stat Med. 1999;18(17-18):2529-45. doi: 10.1002/(sici)1097-0258(19990915/30)18:17/18<2529::aid-sim274>3.0.co;2-5.

基于非参数接收器操作特征的生存结局评估。

Nonparametric receiver operating characteristic-based evaluation for survival outcomes.

机构信息

Department of Epidemiology and Biostatistics, University of Georgia, Athens, GA 30602, USA.

出版信息

Stat Med. 2012 Oct 15;31(23):2660-75. doi: 10.1002/sim.5386.

DOI:10.1002/sim.5386
PMID:22987578
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3743052/
Abstract

For censored survival outcomes, it can be of great interest to evaluate the predictive power of individual markers or their functions. Compared with alternative evaluation approaches, approaches based on the time-dependent receiver operating characteristics (ROC) rely on much weaker assumptions, can be more robust, and hence are preferred. In this article, we examine evaluation of markers' predictive power using the time-dependent ROC curve and a concordance measure that can be viewed as a weighted area under the time-dependent area under the ROC curve profile. This study significantly advances from existing time-dependent ROC studies by developing nonparametric estimators of the summary indexes and, more importantly, rigorously establishing their asymptotic properties. It reinforces the statistical foundation of the time-dependent ROC-based evaluation approaches for censored survival outcomes. Numerical studies, including simulations and application to an HIV clinical trial, demonstrate the satisfactory finite-sample performance of the proposed approaches.

摘要

对于删失生存结局,评估个体标志物的预测能力或其功能可能非常有意义。与替代评估方法相比,基于时依接收器工作特征(ROC)的方法依赖于较弱的假设,可以更稳健,因此是首选的。在本文中,我们使用时依 ROC 曲线和一致性度量来检查标志物预测能力的评估,可以将一致性度量视为时依 ROC 曲线下面积的加权轮廓下面积。本研究通过开发摘要指标的非参数估计量,从现有的时依 ROC 研究中取得了重大进展,更重要的是,严格确立了它们的渐近性质。它加强了基于时依 ROC 的删失生存结局评估方法的统计基础。数值研究,包括模拟和对 HIV 临床试验的应用,表明了所提出方法在有限样本下的良好性能。