RT Journal Article
T1 Bayesian analysis of a disability model for lung cancer survival
A1 Armero, C.
A1 Cabras, Stefano
A1 Castellanos, M. E.
A1 Perra, S.
A1 Quirós, A.
A1 Oruezabal, M. J.
A1 Sanchez-Rubio, J.
AB 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.
PB SAGE
SN 0962-2802
YR 2016
FD 2016-02-01
LK https://hdl.handle.net/10016/33011
UL https://hdl.handle.net/10016/33011
LA eng
NO This study has been partially supported by the Ministerio de Ciencia e Innovación [grant number MTM2010- 19528], Mutua Madrileña [grant AP75942010], Ministero dell'Istruzione, dell'Universitá e della Ricerca of Italy and the visiting professor program of the Regione Autonoma della Sardegna.
DS e-Archivo
RD 1 sept. 2024