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Article
Steady State Gibbs Sampler Estimation for Lung Cancer Data
Journal of Applied Statistics
  • Martin X. Dunbar, Abbott Laboratories
  • Hani Samawi, Georgia Southern University
  • Robert L. Vogel, Georgia Southern University
  • Lili Yu, Georgia Southern University
Document Type
Article
Publication Date
1-1-2014
DOI
10.1080/02664763.2013.830285
Disciplines
Abstract
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. [13] 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. [3], 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.
Citation Information
Martin X. Dunbar, Hani Samawi, Robert L. Vogel and Lili Yu. "Steady State Gibbs Sampler Estimation for Lung Cancer Data" Journal of Applied Statistics Vol. 41 Iss. 1 (2014) p. 109 - 117
Available at: http://works.bepress.com/robert_vogel/58/