6533b831fe1ef96bd12999fc
RESEARCH PRODUCT
Bayesian analysis of a disability model for lung cancer survival
Silvia PerraStefano CabrasCarmen ArmeroAlicia QuirósMaria Eugenia CastellanosJ Sanchez-rubioM Oruezábalsubject
Statistics and ProbabilityLung NeoplasmsEpidemiologyComputer scienceMatemáticasPosterior probabilityBayesian probabilityEstadísticaBiostatisticsAccelerated failure time modelsBayesian inference01 natural sciences010104 statistics & probability03 medical and health sciencesBayes' theoremsymbols.namesake0302 clinical medicineHealth Information ManagementBayesian information criterionCarcinoma Non-Small-Cell LungStatisticsPrior probabilityHumans0101 mathematicsBiología y BiomedicinaNeoplasm StagingInformáticaBayes estimatorBayes TheoremMarkov chain Monte CarloSurvival AnalysisBayesian information criterionMarkov Chains030220 oncology & carcinogenesisMinimum informative priorsymbolsMulti-state modelsRegression AnalysisWeibull distributionMonte Carlo Methoddescription
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. 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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-01-01 |