Demarqui Fabio N, Dey Dipak K, Loschi Rosangela H, Colosimo Enrico A
Departamento de Estatística, Universidade Federal de Minas Gerais, Avenida Presidente Antônio Carlos, 6627, CEP 31270-901, Belo Horizonte-MG, Brasil.
Biom J. 2014 Mar;56(2):198-218. doi: 10.1002/bimj.201200205. Epub 2013 Dec 16.
In this paper, we consider a piecewise exponential model (PEM) with random time grid to develop a full semiparametric Bayesian cure rate model. An elegant mechanism enjoying several attractive features for modeling the randomness of the time grid of the PEM is assumed. To model the prior behavior of the failure rates of the PEM we assume a hierarchical modeling approach that allows us to control the degree of parametricity in the right tail of the survival curve. Properties of the proposed model are discussed in detail. In particular, we investigate the impact of assuming a random time grid for the PEM on the estimation of the cure fraction. We further develop an efficient collapsed Gibbs sampler algorithm for carrying out posterior computation. A Bayesian diagnostic method for assessing goodness of fit and performing model comparisons is briefly discussed. Finally, we illustrate the usefulness of the new methodology with the analysis of a melanoma clinical trial that has been discussed in the literature.
在本文中，我们考虑一个具有随机时间网格的分段指数模型（PEM），以开发一个完整的半参数贝叶斯治愈率模型。我们假定了一种优雅的机制，该机制对于模拟PEM时间网格的随机性具有几个吸引人的特征。为了对PEM失效率的先验行为进行建模，我们采用了一种分层建模方法，该方法使我们能够控制生存曲线右尾的参数化程度。详细讨论了所提出模型的性质。特别是，我们研究了为PEM假定随机时间网格对治愈率估计的影响。我们进一步开发了一种有效的折叠吉布斯采样器算法来进行后验计算。简要讨论了一种用于评估拟合优度和进行模型比较的贝叶斯诊断方法。最后，我们通过对文献中讨论的一项黑色素瘤临床试验的分析来说明新方法的实用性。
