Cure fraction models using mixture and non-mixture models

Achcar, Jorge A.; Coelho-Barros, Emílio A.; Mazucheli, Josmar

doi:10.2478/v10127-012-0001-4

$Cure fraction models using mixture and non-mixture models Cover$

.blurhash-client-img { display: none !important; }

Cure fraction models using mixture and non-mixture models

Tatra Mountains Mathematical Publications

Volume 51 (2012): Issue 1 (April 2012)

By: Jorge A. Achcar, Emílio A. Coelho-Barros and Josmar Mazucheli

Open Access

|Nov 2012

Abstract

We introduce the Weibull distributions in presence of cure fraction, censored data and covariates. Two models are explored in this paper: mixture and non-mixture models. Inferences for the proposed models are obtained under the Bayesian approach, using standard MCMC (Markov Chain Monte Carlo) methods. An illustration of the proposed methodology is given considering a life- time data set.

References

[1] CHIB, S.-GREENBERG, E.: Understanding the metropolis-hastings algorithm, Amer. Statist. 49 (1995), 327-335.
Search in Google Scholar
[2] GELFAND, A. E.-SMITH, A. F. M.: Sampling-based approaches to calculating marginaldensities, J. Amer. Statist. Assoc. 85 (1990), 398-409.10.1080/01621459.1990.10476213
Search in Google Scholar
[3] KLEIN, J. P.-MOESCHBERGER, M. L.: Survival Analysis: Techniques for Censored and Truncated Data. Springer-Verlag, New York, NY, 1997.
Search in Google Scholar
[4] LAMBERT, P. C.-DICKMAN, P. W.-WESTON, C. L.-THOMPSON, J. R.: Estimatingthe cure fraction in population-based cancer studies by using finite mixture models, J. Roy. Stat. Soc. Ser. C 59 (2010), 35-55.10.1111/j.1467-9876.2009.00677.x
Search in Google Scholar
[5] MALLER, R. A.-ZHOU, X.: Survival Analysis with Long-Term Survivors. John Wiley & Sons Ltd., Chichester, 1996.
Search in Google Scholar
[6] SAS: The MCMC Procedure, SAS/STATR User’s Guide, Version 9.22, Cary, NC: SAS Institute Inc., 2010, 4102-4326.
Search in Google Scholar
[7] SPIEGELHALTER, D.-BEST, N.-CARLIN, B.-VAN DER LINDE, A.: Bayesianmeasures of model complexity and fit, J. R. Stat. Soc. Ser. B, Methodol. 64 (2002), 583-639.
Search in Google Scholar
[8] TSODIKOV, A. D.-IBRAHIM, J. G.-YAKOVLEV, A. Y.: Estimating cure rates fromsurvival data: an alternative to two-component mixture models, J. Amer. Statist. Assoc. 98 (2003), 1063-1078.10.1198/01622145030000001007299877121151838
Search in Google Scholar
[9] WIENKE, A.-LOCATELLI, I.-YASHIN, A. I.: The modelling of a cure fraction inbivariate time-to-event data, Austrian J. Statist. 35 (2006), 67-76.
Search in Google Scholar
[10] YAKOVLEV, A.-TSODIKOV, A. D.: Stochastic Models of Tumor Latency and Their Biostatistical Applications, World Scientific, Singapore, 1996. 10.1142/2420
Search in Google Scholar

DOI: https://doi.org/10.2478/v10127-012-0001-4 | Journal eISSN: 1338-9750 | Journal ISSN: 12103195

Journal RSS Feed

Language: English

Page range: 1 - 9

Published on: Nov 13, 2012

Published by: Slovak Academy of Sciences, Mathematical Institute

In partnership with: Paradigm Publishing Services

Publication frequency: 3 issues per year

Keywords:

cure fraction,

mixture model,

non-mixture model,

Weibull distribution,

lifetime data.

Related subjects:

Mathematics,

General mathematics

© 2012 Jorge A. Achcar, Emílio A. Coelho-Barros, Josmar Mazucheli, published by Slovak Academy of Sciences, Mathematical Institute
This work is licensed under the Creative Commons License.

Volume 51 (2012): Issue 1 (April 2012)Next article