In non-Markov multi-state models, the traditional Aalen–Johansen (AJ) estimator for state transition probabilities is generally not valid. An alternative, suggested by Putter and Spitioni, is to analyse a subsample of the full data, consisting of the individuals present in a specific state at a given landmark time-point. The AJ estimator of occupation probabilities is then applied to the landmark subsample. Exploiting the result by Datta and Satten, that the AJ estimator is consistent for state occupation probabilities even in non-Markov models given that censoring is independent of state occupancy and times of transition between states, the landmark Aalen–Johansen (LMAJ) estimator provides consistent estimates of transition probabilities. So far, this approach has only been studied for non-parametric estimation without covariates. In this paper, we show how semi-parametric regression models and inverse probability weights can be used in combination with the LMAJ estimator to perform covariate adjusted analyses. The methods are illustrated by a simulation study and an application to population-wide registry data on work, education and health-related absence in Norway. Results using the traditional AJ estimator and the LMAJ estimator are compared, and show large differences in estimated transition probabilities for highly non-Markov multi-state models.

中文翻译：

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具有协变量的非马尔可夫多状态模型中转移概率的地标估计。

在非马尔可夫多状态模型中，状态转移概率的传统Aalen-Johansen（AJ）估计器通常无效。Putter和Spitioni建议，另一种方法是分析完整数据的子样本，该子样本由在给定界标时间点处于特定状态的个体组成。然后将职业概率的AJ估计器应用于界标子样本。利用Datta和Satten的结果，即使在非马尔可夫模型中，即使在非马尔可夫模型中，AJ估计量对于状态占领概率也是一致的，因为审查独立于状态占用和状态之间的转换时间，具有里程碑意义的Aalen-Johansen（LMAJ）估计量提供了一致过渡概率的估计。到目前为止，仅针对没有协变量的非参数估计研究了这种方法。在本文中，我们展示了如何将半参数回归模型和逆概率权重与LMAJ估计量结合使用以执行协变量调整后的分析。通过模拟研究说明了这些方法，并将其应用于挪威全国范围内有关工作，教育和与健康相关的缺勤的人口登记数据。比较了使用传统AJ估计器和LMAJ估计器的结果，结果表明，对于高度非马尔可夫多态模型，估计的转移概率存在很大差异。