References
- Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In: Petrov, B. N., Csáki, F., eds. 2nd International Symposium on Information Theory, Budapest: Akadémiai Kiadó, pp. 267–281.
- Balakrishnan, N., Pal, S. (2012). EM algorithm-based likelihood estimation for some cure rate models. Journal of Statistical Theory and Practice 6:698–724.
- Balakrishnan, N., Pal, S. (2013). Lognormal lifetimes and likelihood-based inference for flexible cure rate models based on COM-Poisson family. Computational Statistics & Data Analysis 67:41–67.
- Balakrishnan, N., Pal, S. (2016). Expectation maximization-based likelihood inference for flexible cure rate models with Weibull lifetimes. Statistical Methods in Medical Research 25(4):1535–1563.
- Balakrishnan, N., Pal, S. (2015). An EM algorithm for the estimation of parameters of a flexible cure rate model with generalized gamma lifetime and model discrimination using likelihood and information-based methods. Computational Statistics 30:151–189.
- Berkson, J., Gage, R. (1952). Survival curve for cancer patients following treatment. Journal of the American Statistical Association 47:501–515.
- Cancho, V. G., de Castro, M., Dey, D. K. (2013a). Long-term survival models with latent activation under a flexible family of distributions. Brazilian Journal of Probability and Statistics 27(4):585–600.
- Cancho, V. G., Louzada, F., Barriga, G. D. C. (2013b). The geometric Birnbaum-Saunders regression model with cure rate. Journal of Statistical Planning and Inference 142:993–1000.
- Cancho, V. G., Louzada, F., Ortega, E. M. (2013c). The power series cure rate model: An application to a cutaneous melanoma data. Communications in Statistics - Simulation and Computation 42:586–602.
- Cancho, V. G., Rodrigues, J., de Castro, M. (2011). A flexible model for survival data with a cure rate: A Bayesian approach. Journal of Applied Statistics 38:57–70.
- 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 94:909–919.
- Cooner, F., Banerjee, S., Carlin, B. P., Sinha, D. (2007). Flexible cure rate modeling under latent activation schemes. Journal of the American Statistical Association 102:560–572.
- Demarqui, F. N., Dey, D. K., Loschi, R. H., Colosimo, E. A. (2014). Fully semiparametric Bayesian approach for modeling survival data with cure fraction. Biometrical Journal 56:198–218.
- Drzewiecki, K. T., Ladefoged, C., Christensen, H. E. (1980). Biopsy and prognosis for cutaneous malignant melanomas in clinical stage I. Scandinavian Journal of Plastic and Reconstructive Surgery and Hand Surgery 14(2):141–144.
- Dunn, P. K., Smyth, G. K. (1996). Randomized quantile residuals. Journal of Computational and Graphical Statistics 5:236–244.
- Gallardo, D. I., Bolfarine, H., Pedroso-de-Lima, A. C. (2013). Promotion time cure rate model with random effects: An application to a multi-centre clinical trial of carcinoma. Statistics Research Letters (SRL) 2(2):44–52.
- Gallardo, D. I., Bolfarine, H., Pedroso-de-Lima, A. C. (2016). Promotion time cure rate model with bivariate random effects. Communications in Statistics - Simulation and Computation 45(2):603–624.
- Ibrahim, J. G., Chen, M.-H., Sinha, D. (2001). Bayesian semiparametric models for survival data with a cure fraction. Biometrics 57:383–388.
- Kim, S., Chen, M.-H., Dey, D. K., Gamerman, D. (2007). Bayesian dynamic models for survival data with a cure fraction. Lifetime Data Analysis 13:17–35.
- Li, C. S., Taylor, J., Sy, J. (2001). Identifiability of cure models. Statistics and Probability Letters 54:389–395.
- Lopes, C. M. C., Bolfarine, H. (2012). Random effects in promotion time cure rate models. Computational Statistics & Data Analysis 56(1):75–87.
- Noack, A. (1950). On a class of discrete random variables. Annals of Mathematical Statistics 21:127–132.
- Ortega, E. M. M., Barriga, G. D. C., Hashimoto, E. M., Cancho, V. G., Cordeiro, G. M. (2014). A new class of survival regression models with cure fraction. Journal of Data Science 12:107–136.
- R Core Team (2016). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available at: https://www.R-project.org/.
- Rodrigues, J., Cancho, V. G., de Castro, M. A., Louzada-Neto, F. (2009). On the unification of the long-term survival models. Statistics and Probability Letters 79:753–759.
- Rodrigues, J., de Castro, M., Balakrishnan, N., Cancho, V. G. (2011). Destructive weighted Poisson cure rate models. Lifetime Data Analysis 17:333–346.
- Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics 6:461–464.
- Yakovlev, A. Y., Tsodikov, A. D. (1996). Stochastic Models of Tumor Latency and their Biostatistical Applications. New Jersey: World Scientific.
- Yin, G., Ibrahim, J. G. (2005). Cure rate models: An unified approach. Canadian Journal of Statistics 33(4):559–570.