Modelling survival data to account for model uncertainty: a single model or model averaging?


Autoria(s): Thamrin, Sri Astuti; McGree, James Matthew; Mengersen, Kerrie L.
Data(s)

11/12/2013

Resumo

This study considered the problem of predicting survival, based on three alternative models: a single Weibull, a mixture of Weibulls and a cure model. Instead of the common procedure of choosing a single “best” model, where “best” is defined in terms of goodness of fit to the data, a Bayesian model averaging (BMA) approach was adopted to account for model uncertainty. This was illustrated using a case study in which the aim was the description of lymphoma cancer survival with covariates given by phenotypes and gene expression. The results of this study indicate that if the sample size is sufficiently large, one of the three models emerge as having highest probability given the data, as indicated by the goodness of fit measure; the Bayesian information criterion (BIC). However, when the sample size was reduced, no single model was revealed as “best”, suggesting that a BMA approach would be appropriate. Although a BMA approach can compromise on goodness of fit to the data (when compared to the true model), it can provide robust predictions and facilitate more detailed investigation of the relationships between gene expression and patient survival. Keywords: Bayesian modelling; Bayesian model averaging; Cure model; Markov Chain Monte Carlo; Mixture model; Survival analysis; Weibull distribution

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/66427/

Publicador

Springer

Relação

http://eprints.qut.edu.au/66427/1/manuscript_eprint..pdf

DOI:10.1186/2193-1801-2-665

Thamrin, Sri Astuti, McGree, James Matthew, & Mengersen, Kerrie L. (2013) Modelling survival data to account for model uncertainty: a single model or model averaging? SpringerPlus, 2(665), pp. 1-13.

Direitos

Copyright 2013 Thamrin et al.; licensee Springer.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Fonte

School of Mathematical Sciences; Science & Engineering Faculty

Palavras-Chave #010401 Applied Statistics #Bayesian modelling #Bayesian model averaging #Cure model #Markov Chain Monte Carlo #Mixture model #Survival analysis #Weibull distribution
Tipo

Journal Article