Meira-Machado Luís, Cadarso-Suárez Carmen, de Uña-Alvarez Jacobo
Department of Mathematics for Science and Technology, University of Minho, 4810 Azurém, Guimarães, Portugal.
Comput Methods Programs Biomed. 2007 May;86(2):131-40. doi: 10.1016/j.cmpb.2007.01.010. Epub 2007 Mar 9.
The aim of this paper is to present an R library, called tdc.msm, developed to analyze multi-state survival data. In this library, the time-dependent regression model and multi-state models are included as two possible approaches for such data. For the multi-state modelling five different models are considered, allowing the user to choose between Markov and semi-Markov property, as well as to use homogeneous or non-homogeneous models. Specifically, the following multi-state models in continuous time were implemented: Cox Markov model; Cox semi-Markov model; homogeneous Markov model; non-homogeneous piecewise model and non-parametric Markov model. This software can be used to fit multi-state models with one initial state (e.g., illness diagnosis), a finite number of intermediate states, representing, for example, a change of treatment, and one absorbing state corresponding to a terminal event of interest. Graphical output includes survival estimates, transition probabilities estimates and smooth log hazard for continuous covariates.
本文的目的是展示一个名为tdc.msm的R库，该库是为分析多状态生存数据而开发的。在这个库中，时间依赖回归模型和多状态模型被作为处理此类数据的两种可能方法。对于多状态建模，考虑了五种不同的模型，允许用户在马尔可夫和半马尔可夫性质之间进行选择，以及使用齐次或非齐次模型。具体而言，实现了以下连续时间的多状态模型：考克斯马尔可夫模型；考克斯半马尔可夫模型；齐次马尔可夫模型；非齐次分段模型和非参数马尔可夫模型。该软件可用于拟合具有一个初始状态（例如疾病诊断）、有限数量的中间状态（例如表示治疗变化）以及一个对应于感兴趣的终端事件的吸收状态的多状态模型。图形输出包括生存估计、转移概率估计以及连续协变量的平滑对数风险。
