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A two-component Weibull mixture to model early and late mortality in a Bayesian framework

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  • Farcomeni, Alessio
  • Nardi, Alessandra

Abstract

A two-component parametric mixture is proposed to model survival after an invasive treatment, when patients may experience different hazards regimes: a risk of early mortality directly related to the treatment and/or the treated condition, and a risk of late death influenced by several exogenous factors. The parametric mixture is based on Weibull distributions for both components. Different sets of covariates can affect the Weibull scale parameters and the probability of belonging to one of the two latent classes. A logarithmic function is used to link explanatory variables to scale parameters while a logistic link is assumed for the probability of the latent classes. Inference about unknown parameters is developed in a Bayesian framework: point and interval estimates are based on posterior distributions, whereas the Schwarz criterion is used for testing hypotheses. The advantages of the approach are illustrated by analyzing data from an aorta aneurysm study.

Suggested Citation

  • Farcomeni, Alessio & Nardi, Alessandra, 2010. "A two-component Weibull mixture to model early and late mortality in a Bayesian framework," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 416-428, February.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:2:p:416-428
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    References listed on IDEAS

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    1. Carrasco, Jalmar M.F. & Ortega, Edwin M.M. & Cordeiro, Gauss M., 2008. "A generalized modified Weibull distribution for lifetime modeling," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 450-462, December.
    2. R. Jiang & D. N. P. Murthy, 1998. "Mixture of Weibull distributions—parametric characterization of failure rate function," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 14(1), pages 47-65, March.
    3. van Wieringen, Wessel N. & Kun, David & Hampel, Regina & Boulesteix, Anne-Laure, 2009. "Survival prediction using gene expression data: A review and comparison," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1590-1603, March.
    4. Alessandra Nardi & Michael Schemper, 1999. "New Residuals for Cox Regression and Their Application to Outlier Screening," Biometrics, The International Biometric Society, vol. 55(2), pages 523-529, June.
    5. Yu, Binbing & Peng, Yingwei, 2008. "Mixture cure models for multivariate survival data," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1524-1532, January.
    6. W. R. Gilks & N. G. Best & K. K. C. Tan, 1995. "Adaptive Rejection Metropolis Sampling Within Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 44(4), pages 455-472, December.
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    1. Manuel Franco & Narayanaswamy Balakrishnan & Debasis Kundu & Juana-María Vivo, 2014. "Generalized mixtures of Weibull components," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 515-535, September.

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