Putter H, Fiocco M, Geskus R B
Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.
Stat Med. 2007 May 20;26(11):2389-430. doi: 10.1002/sim.2712.
Standard survival data measure the time span from some time origin until the occurrence of one type of event. If several types of events occur, a model describing progression to each of these competing risks is needed. Multi-state models generalize competing risks models by also describing transitions to intermediate events. Methods to analyze such models have been developed over the last two decades. Fortunately, most of the analyzes can be performed within the standard statistical packages, but may require some extra effort with respect to data preparation and programming. This tutorial aims to review statistical methods for the analysis of competing risks and multi-state models. Although some conceptual issues are covered, the emphasis is on practical issues like data preparation, estimation of the effect of covariates, and estimation of cumulative incidence functions and state and transition probabilities. Examples of analysis with standard software are shown.
标准生存数据测量从某个时间起点到某类事件发生的时间跨度。如果发生了多种类型的事件，就需要一个描述向每种竞争风险进展情况的模型。多状态模型通过描述向中间事件的转变，对竞争风险模型进行了推广。在过去二十年里已经开发出了分析此类模型的方法。幸运的是，大多数分析可以在标准统计软件包中进行，但可能需要在数据准备和编程方面付出一些额外的努力。本教程旨在回顾用于分析竞争风险和多状态模型的统计方法。虽然涵盖了一些概念性问题，但重点是数据准备、协变量效应估计、累积发病率函数估计以及状态和转移概率估计等实际问题。文中展示了使用标准软件进行分析的示例。
