Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA.
Stat Med. 2013 Jul 30;32(17):3006-19. doi: 10.1002/sim.5703. Epub 2012 Dec 6.
We construct nonparametric regression estimators of a number of temporal functions in a multistate system based on a continuous univariate baseline covariate. These estimators include state occupation probabilities, state entry, exit, and waiting (sojourn) time distribution functions of a general progressive (e.g., acyclic) multistate model. We subject the data to right censoring, and the censoring mechanism is explainable by observable covariates that could be time dependent. The resulting estimators are valid even if the multistate process is non-Markov. We study the performance of the estimators in two simulation settings. We establish large sample consistency of these estimators. We illustrate our estimators using a data set on bone marrow transplant recipients.
我们基于一个连续的单变量基线协变量，为多状态系统中的多个时间函数构建了非参数回归估计量。这些估计量包括一般渐进（例如，无环）多状态模型的状态占据概率、状态进入、退出和等待（逗留）时间分布函数。我们对数据进行右删失，并且删失机制可以由可能随时间变化的可观察协变量来解释。即使多状态过程是非马尔可夫的，得到的估计量也是有效的。我们在两种模拟环境下研究了这些估计器的性能。我们证明了这些估计量的大样本一致性。我们使用骨髓移植受者的数据来展示我们的估计器。
