Hoff Rune, Putter Hein, Mehlum Ingrid Sivesind, Gran Jon Michael
Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo, Norway.
Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.
Lifetime Data Anal. 2019 Oct;25(4):660-680. doi: 10.1007/s10985-019-09474-0. Epub 2019 Apr 17.
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.
在非马尔可夫多状态模型中，用于状态转移概率的传统阿伦 - 约翰森（AJ）估计器通常是无效的。普特和斯皮蒂尼提出的一种替代方法是分析完整数据的一个子样本，该子样本由在给定标志性时间点处于特定状态的个体组成。然后将职业概率的AJ估计器应用于标志性子样本。利用达塔和萨滕的结果，即即使在非马尔可夫模型中，只要删失与状态占用和状态之间的转移时间无关，AJ估计器对于状态占用概率就是一致的，标志性阿伦 - 约翰森（LMAJ）估计器提供了转移概率的一致估计。到目前为止，这种方法仅在无协变量的非参数估计中进行了研究。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估计器结合使用，以进行协变量调整分析。通过模拟研究和对挪威关于工作、教育和与健康相关缺勤的全人群登记数据的应用来说明这些方法。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状态模型，估计的转移概率存在很大差异。
