Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
Biostatistics. 2011 Jul;12(3):535-47. doi: 10.1093/biostatistics/kxq071. Epub 2010 Dec 6.
Recurrent events are the natural outcome in many medical and epidemiology studies. To assess covariate effects on the gaps between consecutive recurrent events, the Cox proportional hazards model is frequently employed in data analysis. The validity of statistical inference, however, depends on the appropriateness of the Cox model. In this paper, we propose a class of graphical techniques and formal tests for checking the Cox model with recurrent gap time data. The building block of our model checking method is an averaged martingale-like process, based on which a class of multiparameter stochastic processes is proposed. This maneuver is very general and can be used to assess different aspects of model fit. Numerical simulations are conducted to examine finite-sample performance, and the proposed model checking techniques are illustrated with data from the Danish Psychiatric Central Register.
在许多医学和流行病学研究中，复发事件是自然的结果。为了评估协变量对连续复发事件之间差距的影响，Cox 比例风险模型经常用于数据分析。然而，统计推断的有效性取决于 Cox 模型的适当性。在本文中，我们提出了一类用于检查具有复发间隔时间数据的 Cox 模型的图形技术和正式检验。我们的模型检验方法的构建块是一个平均似马尔可夫过程，基于此，提出了一类多参数随机过程。这种操作非常通用，可以用于评估模型拟合的不同方面。进行了数值模拟以检查有限样本性能，并使用丹麦精神病学中央登记处的数据说明了所提出的模型检验技术。
