Bayesian analysis of a disability model for lung cancer survival

Author:

Armero C1,Cabras S23,Castellanos ME4,Perra S2,Quirós A5,Oruezábal MJ67,Sánchez-Rubio J8

Affiliation:

1. Departament d'Estadística i Investigació Operativa, Universitat de València, Spain

2. Dipartimento di Matematica e Informatica, Universitá degli Studi di Cagliari, Italy

3. Departamento de Estadística, Universidad Carlos III de Madrid, Spain

4. Departamento de Estadística e Investigación Operativa, Universidad Rey Juan Carlos, Spain

5. Departamento de Teoría de la Señal y Comunicaciones, Universidad Rey Juan Carlos, Spain

6. Unidad Onco-Hematológica, Hospital Universitario Infanta Cristina de Madrid, Spain

7. Servicio Oncología Médica, Hospital Rey Juan Carlos, Spain

8. Servicio de Farmacia, Hospital Universitario Infanta Cristina de Madrid, Spain

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.

Publisher

SAGE Publications

Subject

Health Information Management,Statistics and Probability,Epidemiology

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