Department of Social Medicine, University of São Paulo (USP), Ribeirão Preto School of Medicine, Brazil.
Comput Methods Programs Biomed. 2013 Dec;112(3):343-55. doi: 10.1016/j.cmpb.2013.07.021. Epub 2013 Aug 6.
The cure fraction models are usually used to model lifetime time data with long-term survivors. In the present article, we introduce a Bayesian analysis of the four-parameter generalized modified Weibull (GMW) distribution in presence of cure fraction, censored data and covariates. In order to include the proportion of "cured" patients, mixture and non-mixture formulation models are considered. To demonstrate the ability of using this model in the analysis of real data, we consider an application to data from patients with gastric adenocarcinoma. Inferences are obtained by using MCMC (Markov Chain Monte Carlo) methods.
治愈分率模型通常用于对存在长期幸存者的寿命时间数据进行建模。在本文中，我们介绍了在存在治愈分率、删失数据和协变量的情况下，对四参数广义修正 Weibull（GMW）分布的贝叶斯分析。为了包含“治愈”患者的比例，我们考虑了混合和非混合配方模型。为了演示在分析实际数据时使用此模型的能力，我们考虑了一个来自胃腺癌患者数据的应用。通过使用 MCMC（马尔可夫链蒙特卡罗）方法进行推断。
