Bayesian analysis of a disability model for lung cancer survival Articles uri icon

authors

  • ARMERO, C.
  • CABRAS, STEFANO
  • CASTELLANOS, MARÍA EUGENIA
  • PERRA, S.
  • QUIRÓS, A.
  • ORUEZABAL, M. J.
  • SANCHEZ-RUBIO, J.

publication date

  • February 2016

start page

  • 336

end page

  • 351

issue

  • 1

volume

  • 25

international standard serial number (ISSN)

  • 0962-2802

electronic international standard serial number (EISSN)

  • 1477-0334

abstract

  • Bayesian reasoning, survival analysis and multi-state models are used to assess survival times for Stage IV non-small-cell lung cancer patients and the evolution of the disease over time. Bayesian estimation is done using minimum informative priors for the Weibull regression survival model, leading to an automatic inferential procedure. Markov chain Monte Carlo methods have been used for approximating posterior distributions and the Bayesian information criterion has been considered for covariate selection. In particular, the posterior distribution of the transition probabilities, resulting from the multi-state model, constitutes a very interesting tool which could be useful to help oncologists and patients make efficient and effective decisions.

keywords

  • accelerated failure time models; bayesian information criterion; minimum informative prior; multi-state models; weibull distribution; multistate models; terminal events; time; transplantation; chemotherapy; prediction; histology; choice