Gelfand A E, Mallick B K
Department of Statistics, University of Connecticut, Storrs 06269- 3120, USA.
Biometrics. 1995 Sep;51(3):843-52.
We consider the usual proportional hazards model in the case where the baseline hazard, the covariate link, and the covariate coefficients are all unknown. Both the baseline hazard and the covariate link are monotone functions and thus are characterized using a dense class of such functions which arises, upon transformation, as a mixture of Beta distribution functions. We take a Bayesian approach for fitting such a model. Since interest focuses more upon the likelihood, we consider vague prior specifications including Jeffreys's prior. Computations are carried out using sampling-based methods. Model criticism is also discussed. Finally, a data set studying survival of a sample of lung cancer patients is analyzed.
我们考虑在基线风险、协变量链接和协变量系数均未知的情况下的常用比例风险模型。基线风险和协变量链接都是单调函数，因此使用此类函数的一个密集类来表征，经过变换后，该密集类作为贝塔分布函数的混合出现。我们采用贝叶斯方法来拟合这样一个模型。由于兴趣更多地集中在似然性上，我们考虑包括杰弗里斯先验在内的模糊先验规范。使用基于抽样的方法进行计算。还讨论了模型批评。最后，分析了一个研究肺癌患者样本生存情况的数据集。
