Multi-state models are frequently used when data come from subjects observed over time and where focus is on the occurrence of events that the subjects may experience. A convenient modeling assumption is that the multi-state stochastic process is Markovian, in which case a number of methods are available when doing inference for both transition intensities and transition probabilities. The Markov assumption, however, is quite strict and may not fit actual data in a satisfactory way. Therefore, inference methods for non-Markov models are needed. In this paper, we review methods for estimating transition probabilities in such models and suggest ways of doing regression analysis based on pseudo observations. In particular, we will compare methods using land-marking with methods using plug-in. The methods are illustrated using simulations and practical examples from medical research.

当数据来自随着时间的推移观察到的受试者并且重点是受试者可能经历的事件的发生时，经常使用多状态模型。一个方便的建模假设是多状态随机过程是马尔可夫过程，在这种情况下，在对转换强度和转换概率进行推断时，可以使用多种方法。然而，马尔可夫假设非常严格，可能无法以令人满意的方式拟合实际数据。因此，需要非马尔可夫模型的推理方法。在本文中，我们回顾了在此类模型中估计转移概率的方法，并提出了基于伪观察进行回归分析的方法。特别是，我们将比较使用地标的方法和使用插件的方法。