References
- Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/https://doi.org/10.1109/TAC.1974.1100705
- Altman, D., Stavola, B. D., Love, S., & Stepniewska, K. (1995). Review of survival analyses published in cancer journals. British Journal of Cancer, 72(2), 511–518. https://doi.org/https://doi.org/10.1038/bjc.1995.364
- Berliner, L. M., & Hill, B. M. (1988). Bayesian nonparametric survival analysis. Journal of the American Statistical Association, 83(403), 772–779. https://doi.org/https://doi.org/10.1080/01621459.1988.10478660
- Brard, C., Le Teuff, G., Le Deley, M.-C., & Hampson, L. V. (2017). Bayesian survival analysis in clinical trials: What methods are used in practice? Clinical Trials (London, England), 14(1), 78–87. https://doi.org/https://doi.org/10.1177/1740774516673362
- Chang, I.-S., Hsiung, C. A., Wu, Y.-J., & Yang, C.-C. (2005). Bayesian survival analysis using Bernstein polynomials. Scandinavian Journal of Statistics, 32(3), 447–466. https://doi.org/https://doi.org/10.1111/j.1467-9469.2005.00451.x
- Chen, M.-H., Ibrahim, J. G., & Sinha, D. (1999). A new Bayesian model for survival data with a surviving fraction. Journal of the American Statistical Association, 94(447), 909–919. https://doi.org/https://doi.org/10.1080/01621459.1999.10474196
- Garay, A. M., Bolfarine, H., Lachos, V. H., & Cabral, C. R. (2015). Bayesian analysis of censored linear regression models with scale mixtures of normal distributions. Journal of Applied Statistics, 42(12), 2694–2714. https://doi.org/https://doi.org/10.1080/02664763.2015.1048671
- Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis (3rd ed.). CRC Press.
- Guure, C., Ibrahim, N. A., & Adam, M. B. (2013). Bayesian inference of the Weibull model based on interval-censored survival data. Computational and Mathematical Methods in Medicine, 2013(849520), 1–10. https://doi.org/https://doi.org/10.1155/2013/849520
- Ibrahim, J., Chen, M.-H., & Berlin, D. S. (2001). Bayesian survival analysis. International Journal of Epidemiology, 31, 479.
- Ibrahim, J., Chen, M.-H., & Sinha, D. (2003). Bayesian survival analysis. The Indian Journal of Statistics, 65(3), 710–711.
- Ibrahim, J., Chen, M.-H., & Sinha, D. (2004). Bayesian survival analysis. Journal of the American Statistical Association, 99(468), 1202–1203.
- Khan, Y., & Khan, A. (2013a). Bayesian analysis of Weibull and log-normal survival models with censoring mechanism. International Journal of Applied Mathematics, 26(6), 671–683.
- Khan, Y., & Khan, A. A. (2013b). Bayesian survival analysis of regression model using Weibull. International Journal of Innovative Research in Science, Engineering and Technology, 2(12), 7199–7204.
- Royston, P. (2001). The lognormal distribution as a model for survival time in cancer, with an Emphasis on Prognostic Factors. Statistica Neerlandica, 55(1), 89–104. https://doi.org/https://doi.org/10.1111/1467-9574.00158
- Sharma, M., Kanwal, S., Bhan, A., & Goyal, A. (2018). Computer based diagnosis of leukemia in blood samples using improved region based deformable models [Paper presentation]. 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), 1437–1441. IEEE. https://doi.org/https://doi.org/10.1109/ICOEI.2018.8553737
- Stan Development Team. (2019). RStan: The R interface to Stan. R package version 2.19.2.
- Sugiura, N. (1978). Further analysts of the data by Akaike’ s information criterion and the finite corrections. Communications in Statistics - Theory and Methods, 7(1), 13–26. https://doi.org/https://doi.org/10.1080/03610927808827599
- Therneau, T. M., Lumley, T. (2014). Package ‘survival’. https://CRAN.R-project.org/package=survival.
- Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research, 11, 3571–3594.
- World Health Organization. (2014). World Cancer Report 2014. Technical report, World Health Organization.