Efficient estimation for the proportional hazards model with competing risks and current status data

The proportional hazards model is the most commonly used model in regression analysis of failure time data and has been discussed by many authors under various situations (Kalbfleisch & Prentice, 2002. The Statistical Analysis of Failure Time Data, Wiley, New York). This paper considers the fitting of the model to current status data when there exist competing risks, which often occurs in, for example, medical Studies. The maximum likelihood estimates of the unknown parameters are derived and their consistency and convergence rate are established. Also we show that the estimates of regression coefficients are efficient and have asymptotically normal distributions. Simulation studies are conducted to assess the finite sample properties of the estimates and an illustrative example is provided. The Canadian Journal of Statistics 37: 592-606; 2009 (C) 2009 Statistical Society of Canada

比例风险模型是失效时间数据回归分析中最常用的模型，许多作者在各种情况下都对其进行了讨论（卡尔弗莱施和普伦蒂斯，2002年，《失效时间数据的统计分析》，威利出版社，纽约）。本文考虑当存在竞争风险时将该模型拟合到现况数据的情况，这在例如医学研究中经常出现。推导了未知参数的最大似然估计，并确定了它们的一致性和收敛速度。我们还表明回归系数的估计是有效的，并且具有渐近正态分布。进行了模拟研究以评估估计的有限样本性质，并提供了一个说明性的例子。《加拿大统计学杂志》37卷：592 - 606页；2009年（C）2009年加拿大统计学会