Multi-state models for the analysis of time-to-event data
Multi-state models for the analysis of time-to-event data
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DOI：
10.1177/0962280208092301
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发表时间：
2009-04-01
影响因子：
2.3
通讯作者：
Andersen, Per K.
中科院分区：
文献类型：
--
作者：
Meira-Machado, Luis;de Una-Alvarez, Jacobo;Andersen, Per K.
生存研究中患者的经历可以建模为一个具有两种状态和一种可能从“活着”状态过渡到“死亡”状态的过程。然而，在一些研究中，“活着”的状态可能被划分为两个或更多个中间（短暂）状态，每个状态对应于疾病的特定阶段。在这些研究中，多状态模型可以用于对患者在各种状态之间的运动进行建模。在这些模型中，感兴趣的问题包括估计进展率，评估个体风险因素的影响，生存率或预后预测。在这篇文章中，我们回顾了多状态模型的建模方法，我们专注于数量的估计，这是转移概率和生存概率。这些方法之间的差异进行了讨论，集中在每种方法可能的优点和缺点。我们还审查了现有的软件，目前可用于适应各种模型，并提出了新的软件开发的R库的形式来分析这些模型。使用来自斯坦福大学心脏移植研究的数据和来自西班牙加利西亚进行的乳腺癌研究的数据说明了不同的方法和软件。
The experience of a patient in a survival study may be modelled as a process with two states and one possible transition from an "alive" state to a "dead" state. In some studies, however, the "alive" state may be partitioned into two or more intermediate (transient) states, each of which corresponding to a particular stage of the illness. In such Studies, multi-state models can be used to model the movement of patients among the various states. In these models issues, of interest include the estimation of progression rates, assessing the effects of individual risk factors, survival rates or prognostic forecasting. In this article, we review modelling approaches for multi-state models, and we focus on the estimation of quantities Such is the transition probabilities and survival probabilities. Differences between these approaches are discussed, focussing on possible advantages and disadvantages for each method. We also review the existing software currently available to fit the various models and present new software developed in the form of an R library to analyse such models. Different approaches and software are illustrated using data from the Stanford heart transplant study and data from a study on breast cancer conducted in Galicia, Spain.