de Castro Mário, Chen Ming-Hui, Zhang Yuanye
Universidade de São Paulo, Instituto de Ciências Matemáticas e de Computação, São Carlos, SP, Brazil.
Department of Statistics, University of Connecticut, Storrs, Connecticut, U.S.A.
Biometrics. 2015 Sep;71(3):760-71. doi: 10.1111/biom.12298. Epub 2015 Mar 11.
Multi-state models can be viewed as generalizations of both the standard and competing risks models for survival data. Models for multi-state data have been the theme of many recent published works. Motivated by bone marrow transplant data, we propose a Bayesian model using the gap times between two successive events in a path of events experienced by a subject. Path specific frailties are introduced to capture the dependence structure of the gap times in the paths with two or more states. Under improper prior distributions for the parameters, we establish propriety of the posterior distribution. An efficient Gibbs sampling algorithm is developed for drawing samples from the posterior distribution. An extensive simulation study is carried out to examine the empirical performance of the proposed approach. A bone marrow transplant data set is analyzed in detail to further demonstrate the proposed methodology.
多状态模型可被视为生存数据的标准模型和竞争风险模型的推广。多状态数据模型一直是近期许多已发表作品的主题。受骨髓移植数据的启发，我们提出了一种贝叶斯模型，该模型使用个体经历的一系列事件路径中两个连续事件之间的间隔时间。引入路径特定的脆弱性以捕捉具有两个或更多状态的路径中间隔时间的依赖结构。在参数的不合适先验分布下，我们建立了后验分布的恰当性。开发了一种有效的吉布斯采样算法以从后验分布中抽取样本。进行了广泛的模拟研究以检验所提方法的实证性能。详细分析了一个骨髓移植数据集以进一步证明所提方法。
