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Steady-state Gibbs sampler estimation for lung cancer data

Author

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  • Martin X. Dunbar
  • Hani M. Samawi
  • Robert Vogel
  • Lili Yu

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.

Suggested Citation

  • Martin X. Dunbar & Hani M. Samawi & Robert Vogel & Lili Yu, 2014. "Steady-state Gibbs sampler estimation for lung cancer data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(5), pages 977-988, May.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:5:p:977-988
    DOI: 10.1080/02664763.2013.858671
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    References listed on IDEAS

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    1. Chib, Siddhartha & Greenberg, Edward, 1994. "Bayes inference in regression models with ARMA (p, q) errors," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 183-206.
    2. Rafa M. Kasim & Stephen W. Raudenbush, 1998. "Application of Gibbs Sampling to Nested Variance Components Models With Heterogeneous Within-Group Variance," Journal of Educational and Behavioral Statistics, , vol. 23(2), pages 93-116, June.
    3. Marín Díazaraque, Juan Miguel & Rodríguez Bernal, María Teresa & Wiper, Michael Peter, 2003. "Using weibull mixture distributions to model heterogeneous survival data," DES - Working Papers. Statistics and Econometrics. WS ws033208, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Marco Marozzi, 2013. "Adaptive choice of scale tests in flexible two-stage designs with applications in experimental ecology and clinical trials," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(4), pages 747-762.
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