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Steady-state Gibbs sampler estimation for lung cancer data

机译:肺癌数据的稳态Gibbs采样器估计

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This paper is based on the application of a Bayesian model to a clinical trial study to determine a more effective treatment to lower mortality rates and consequently to increase survival times among patients with lung cancer. In this study, Qian et al. strived to determine if a Weibull survival model can be used to decide whether to stop a clinical trial. The traditional Gibbs sampler was used to estimate the model parameters. This paper proposes to use the independent steady-state Gibbs sampling (ISSGS) approach, introduced by Dunbar et al., to improve the original Gibbs sampler in multidimensional problems. It is demonstrated that ISSGS provides accuracy with unbiased estimation and improves the performance and convergence of the Gibbs sampler in this application.
机译:本文基于贝叶斯模型在临床试验研究中的应用,以确定更有效的治疗方法以降低死亡率,从而增加肺癌患者的生存时间。在这项研究中,Qian等。努力确定Weibull生存模型是否可用于决定是否停止临床试验。传统的Gibbs采样器用于估计模型参数。本文建议使用由Dunbar等人介绍的独立稳态吉布斯采样(ISSGS)方法来改进多维问题中的原始吉布斯采样器。事实证明,ISSGS在此应用中提供了无偏估计的准确性,并提高了Gibbs采样器的性能和收敛性。

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