多元区间删失失效时间数据的比例优势模型。
The proportional odds model for multivariate interval-censored failure time data.
作者信息
Chen Man-Hua, Tong Xingwei, Sun Jianguo
机构信息
Department of Statistics, University of Missouri, 146 Middlebush Hall, Columbia, MO 65211, USA.
出版信息
Stat Med. 2007 Dec 10;26(28):5147-61. doi: 10.1002/sim.2907.
The proportional odds model is one of the most commonly used regression models in failure time data analysis and has been discussed by many authors (Appl. Stat. 1983; 32:165-171; J. Am. Stat. Assoc. 1999; 94:125-136; J. Am. Stat. Assoc. 1997; 92:960-967; Biometrics 2000; 56:511-518; J. Am. Stat. Assoc. 2001; 96:1446-1457). It specifies that covariates have multiplicative effects on the odds function and is often used when, for example, the covariate effect diminishes over time. Most of the existing methods for the model are for univariate failure time data. In this paper, we discuss how to fit the proportional odds model to multivariate interval-censored failure time data. For inference, the maximum likelihood approach is developed and evaluated by simulation studies, which suggest that the method works well for practical situations. The method is applied to a set of bivariate interval-censored data arising from an AIDS clinical trial.
比例优势模型是失效时间数据分析中最常用的回归模型之一，许多作者都对其进行过讨论（《应用统计学》，1983年；第32卷：第165 - 171页；《美国统计协会杂志》，1999年；第94卷：第125 - 136页；《美国统计协会杂志》，1997年；第92卷：第960 - 967页；《生物统计学》，2000年；第56卷：第511 - 518页；《美国统计协会杂志》，2001年；第96卷：第1446 - 1457页）。该模型规定协变量对优势函数具有乘法效应，常用于例如协变量效应随时间减弱的情况。现有的该模型方法大多针对单变量失效时间数据。在本文中，我们讨论如何将比例优势模型应用于多变量区间删失失效时间数据。为进行推断，我们开发了最大似然方法，并通过模拟研究进行评估，结果表明该方法在实际情况中效果良好。该方法被应用于一组来自艾滋病临床试验的双变量区间删失数据。
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