Siriwardhana Chathura, Kulasekera K B, Datta Somnath
Department of Complementary and Integrative Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, USA.
Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA.
Lifetime Data Anal. 2018 Jul;24(3):464-491. doi: 10.1007/s10985-017-9403-6. Epub 2017 Aug 17.
Inference for the state occupation probabilities, given a set of baseline covariates, is an important problem in survival analysis and time to event multistate data. We introduce an inverse censoring probability re-weighted semi-parametric single index model based approach to estimate conditional state occupation probabilities of a given individual in a multistate model under right-censoring. Besides obtaining a temporal regression function, we also test the potential time varying effect of a baseline covariate on future state occupation. We show that the proposed technique has desirable finite sample performances and its performance is competitive when compared with three other existing approaches. We illustrate the proposed methodology using two different data sets. First, we re-examine a well-known data set dealing with leukemia patients undergoing bone marrow transplant with various state transitions. Our second illustration is based on data from a study involving functional status of a set of spinal cord injured patients undergoing a rehabilitation program.
在生存分析以及事件发生时间多状态数据中，给定一组基线协变量的情况下，对状态占用概率进行推断是一个重要问题。我们引入了一种基于逆删失概率重加权半参数单指标模型的方法，用于在右删失情况下估计多状态模型中给定个体的条件状态占用概率。除了获得一个时间回归函数外，我们还检验基线协变量对未来状态占用的潜在时变效应。我们表明，所提出的技术具有理想的有限样本性能，并且与其他三种现有方法相比，其性能具有竞争力。我们使用两个不同的数据集来说明所提出的方法。首先，我们重新审视一个著名的数据集，该数据集涉及接受骨髓移植并经历各种状态转变的白血病患者。我们的第二个示例基于一项研究的数据，该研究涉及一组接受康复计划的脊髓损伤患者的功能状态。
