On Share Frailty Cure Model: An Application On Cervical Cancer

Date

2016-03-11

Journal Title

Journal ISSN

Volume Title

Publisher

Department of Statistics, Nasarawa State University Keffi.

Abstract

Survival analyses are greatly used in medical research especially frailty models which are mostly used to account for heterogeneity in time-to-event. Over the years treatment of cancer has progressed with some patients being cured from different type of cancer. Survival analysis is more focused on subjects that are less at risk of recurrences, metastasis or death after the first treatment as these set of subjects are regarded as being cured. The general assumption of standard frailty model is that all subjects have the same frailty. These assumptions ignore the heterogeneity of such frailties and will lead to incorrect results and conclusions. To address the identified deficiencies in previous studies, this research will propose a shared frailty cure model. Shared frailty assumes that within a cluster the value of frailty term is improved with constant and common frailty to all subjects in the same group clusters by measuring the correlation between event times within the cluster, hence representing changes over time in clusters or population heterogeneity. These structures can be achieved by introducing covariates that are rank specific by the process Shared frailty model, addressing the weakness of the cure frailty model by considering the homogeneity in groups or clusters were failure can be similar by having the same frailty.

Description

Keywords

Survival, Frailty, Cure, Cervical Cancer.

Citation

[1]. Arbutiski, T. (1985). A family of multiplicative survival models incorporating a long-term survivorship parameter c as a function of covariates. Communications in Statistics-Theory and Methods, 14(7), 1627-1642. [2]. Berkson, J., & Gage, R. P. (1952). Survival curve for cancer patients following treatment. Journal of the American Statistical Association, 47(259), 501-515. [3]. Boag, J. W. (1949). Maximum likelihood estimates of the proportion of patients cured by cancer therapy. Journal of the Royal Statistical Society. Series B (Methodological), 11(1), 15-53. [4]. Box-Steffensmeier, J. M., Linn, S., & Smidt, C. D. (2014). Analyzing the robustness of semi-parametric duration models for the study of repeated events. Political Analysis, 22(2), 183-204. [5]. Callegaro, A., & Iacobelli, S. (2012). The Cox shared frailty model with log-skew-normal frailties. Statistical Modelling, 12(5), 399-418. [6]. Chen, M.-H., Ibrahim, J. G., & Sinha, D. (2002). Bayesian inference for multivariate survival data with a cure fraction. Journal of Multivariate Analysis, 80(1), 101-126. [7]. Choices, N. (2014). Fibroids-NHS Choices. Women's health, 18, 39. [8]. Clayton, D., & Cuzick, J. (1985). Multivariate generalizations of the proportional hazards model. Journal of the Royal Statistical Society. Series A (General), 82-117. [9]. Cucchetti, A., Ferrero, A., Cescon, M., Donadon, M., Russolillo, N., Ercolani, G., . . . Pinna, A. D. (2014). Cure Model Survival Analysis After Hepatic Resection for Colorectal Liver Metastases. Annals of surgical oncology, 1-7. [10]. Farewell, V. T., & Sprott, D. (1988). The use of a mixture model in the analysis of count data. Biometrics, 1191-1194. [11]. Gorny, K. R., Borah, B. J., Brown, D. L., Woodrum, D. A., Stewart, E. A., & Hesley, G. K. (2014). Incidence of additional treatments in women treated with MR-guided focused US for symptomatic uterine fibroids: review of 138 patients with an average follow-up of 2.8 years. Journal of Vascular and Interventional Radiology, 25(10), 1506-1512. [12]. Health, U. N. I. o. (2007). National Cancer Institute. Dictionary of cancer terms. Retrieved on January, 19. [13]. Hirsch, K., & Wienke, A. (2012). Software for semiparametric shared gamma and log-normal frailty models: An overview. Computer methods and programs in biomedicine, 107(3), 582-597. [14]. Hougaard, P. (1995). Frailty models for survival data. Lifetime data analysis, 1(3), 255-273. [15]. Laska, E. M., & Meisner, M. J. (1992). Nonparametric estimation and testing in a cure model. Biometrics, 1223-1234. [16]. Maetani, S., & Gamel, J. W. (2013). Parametric Cure Model versus Proportional Hazards Model in Survival Analysis of Breast Cancer and Other Malignancies. Advances in Breast Cancer Research, 2013.

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