Semiparametric inference for survival models with step process covariates
Semiparametric inference for survival models with step process covariates
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DOI：
10.1002/cjs.10001
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发表时间：
2009-03-01
影响因子：
0.6
通讯作者：
Laud, Purushottam
中科院分区：
文献类型：
--
作者：
Hanson, Timothy;Johnson, Wesley;Laud, Purushottam
作者认为贝叶斯方法拟合三个半参数生存模型，将时间依赖的协变量是阶梯函数。特别是，这些模型是由于考克斯[考克斯（1972）皇家统计学会杂志，B辑，347 187-208]，普伦蒂斯和Kalbfleisch和考克斯和奥克斯[考克斯和奥克斯（1984）生存数据分析，查普曼和霍尔，伦敦]。由于普伦蒂斯和Kalbfleisch [普伦蒂斯和Kalbfleisch（1979）生物统计学，35，25-39]的模型，已经看到非常有限的使用，给予特别考虑。每个模型中的基线分布的先验被视为Polya树的混合，并且通过标准的马尔可夫链蒙特卡罗方法获得后验推断。他们展示了这三种模型在著名的斯坦福大学心脏移植数据和儿童糖尿病酮症酸中毒急诊室治疗期间脑水肿诊断时机的研究中的实施和比较。他们的整体讨论的一个重要特点是半参数家庭的比较，并在给定的数据集的上下文中的家庭的最终标准为基础的选择。The Canadian Journal of Statistics 37：60-79 2009（C）2009加拿大统计学会
The authors consider Bayesian methods for fitting three semiparametric survival models, incorporating time-dependent covariates that are step functions. In particular, these are models due to Cox [Cox (1972) Journal of the Royal Statistical Society, Series B, 347 187-208], Prentice & Kalbfleisch and Cox & Oakes [Cox & Oakes (1984) Analysis of Survival Data, Chapman and Hall, London]. The model due to Prentice & Kalbfleisch [Prentice & Kalbfleisch (1979) Biometrics, 35, 25-39], which has seen very limited use, is given particular consideration. The prior for the baseline distribution in each model is taken to be a mixture of Polya trees and posterior inference is obtained through standard Markov chain Monte Carlo methods. They demonstrate the implementation and comparison of these three models on the celebrated Stanford heart transplant data and the study of the timing of cerebral edema diagnosis during emergency room treatment of diabetic ketoacidosis in children. An important feature of their overall discussion is the comparison of semi-parametric families, and ultimate criterion based selection of a family within the context of a given data set. The Canadian Journal of Statistics 37: 60-79 2009 (C) 2009 Statistical Society of Canada