Bayesian analysis of a disability model for lung cancer survival

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...

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Bibliographic Details
Published in:Statistical methods in medical research 2016-02, Vol.25 (1), p.336-351
Main Authors: Armero, C, Cabras, S, Castellanos, ME, Perra, S, Quirós, A, Oruezábal, MJ, Sánchez-Rubio, J
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian analysis
Biostatistics
Carcinoma, Non-Small-Cell Lung - mortality
Carcinoma, Non-Small-Cell Lung - pathology
Disability
Humans
Lung cancer
Lung Neoplasms - mortality
Lung Neoplasms - pathology
Markov analysis
Markov Chains
Monte Carlo Method
Monte Carlo simulation
Neoplasm Staging
Oncologists
Regression Analysis
Regression models
Survival
Survival Analysis
Transition probabilities
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Summary: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.
ISSN:0962-2802
1477-0334
DOI:10.1177/0962280212452803