Reich Brian J, Smith Luke B
Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, U.S.A.
Biometrics. 2013 Sep;69(3):651-60. doi: 10.1111/biom.12053. Epub 2013 Jul 11.
In this paper we propose a semiparametric quantile regression model for censored survival data. Quantile regression permits covariates to affect survival differently at different stages in the follow-up period, thus providing a comprehensive study of the survival distribution. We take a semiparametric approach, representing the quantile process as a linear combination of basis functions. The basis functions are chosen so that the prior for the quantile process is centered on a simple location-scale model, but flexible enough to accommodate a wide range of quantile processes. We show in a simulation study that this approach is competitive with existing methods. The method is illustrated using data from a drug treatment study, where we find that the Bayesian model often gives smaller measures of uncertainty than its competitors, and thus identifies more significant effects.
在本文中，我们针对删失生存数据提出了一种半参数分位数回归模型。分位数回归允许协变量在随访期的不同阶段对生存产生不同影响，从而对生存分布进行全面研究。我们采用半参数方法，将分位数过程表示为基函数的线性组合。选择基函数时，使得分位数过程的先验以一个简单的位置 - 尺度模型为中心，但又足够灵活以适应广泛的分位数过程。我们在模拟研究中表明，这种方法与现有方法具有竞争力。使用来自一项药物治疗研究的数据对该方法进行了说明，我们发现贝叶斯模型通常比其竞争对手给出更小的不确定性度量，因此能识别出更显著的效应。
