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Application of a Non-Mixture Cure Rate Model for Analyzing Survival of Patients with Breast Cancer

  • Baghestani, Ahmad Reza (Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences) ;
  • Moghaddam, Sahar Saeedi (Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences) ;
  • Majd, Hamid Alavi (Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences) ;
  • Akbari, Mohammad Esmaeil (Cancer Research Center, Shahid Beheshti University of Medical Sciences) ;
  • Nafissi, Nahid (Cancer Research Center, Shahid Beheshti University of Medical Sciences) ;
  • Gohari, Kimiya (Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences)
  • Published : 2015.11.04

Abstract

Background: As a result of significant progress made in treatment of many types of cancers during the last few decades, there have been an increased number of patients who do not experience mortality. We refer to these observations as cure or immune and models for survival data which include cure fraction are known as cure rate models or long-term survival models. Materials and Methods: In this study we used the data collected from 438 female patients with breast cancer registered in the Cancer Research Center in Shahid Beheshti University of Medical Sciences, Tehran, Iran. The patients had been diagnosed from 1992 to 2012 and were followed up until October 2014. We had to exclude some because of incomplete information. Phone calls were made to confirm whether the patients were still alive or not. Deaths due to breast cancer were regarded as failure. To identify clinical, pathological, and biological characteristics of patients that might have had an effect on survival of the patients we used a non-mixture cure rate model; in addition, a Weibull distribution was proposed for the survival time. Analyses were performed using STATA version 14. The significance level was set at $P{\leq}0.05$. Results: A total of 75 patients (17.1%) died due to breast cancer during the study, up to the last follow-up. Numbers of metastatic lymph nodes and histologic grade were significant factors. The cure fraction was estimated to be 58%. Conclusions: When a cure fraction is not available, the analysis will be changed to standard approaches of survival analysis; however when the data indicate that the cure fraction is available, we suggest analysis of survival data via cure models.

Keywords

References

  1. Abu Bakar MR, Salah KA, Ibrahim NA, et al (2008). Cure fraction, modelling and estimating in a population-based cancer survival analysis. Malaysian J Mathematical Sciences, 2, 113-34.
  2. Achcar JA, Coelho-Barros EA, Mazucheli J (2012). Cure fraction models using mixture and non-mixture models. Tatra Mountains Mathematical Publications, 51, 1-9. https://doi.org/10.2478/v10127-012-0001-4
  3. Akhlaghi AA, Najafi I, Mahmoodi M, et al (2013). Survival analysis of iranian patients undergoing continuous ambulatory peritoneal dialysis using cure model. J Research in Health Sci, 13, 32-6.
  4. Andersson TM, Dickman PW, Eloranta S, et al (2011). Estimating and modelling cure in population-based cancer studies within the framework of flexible parametric survival models. BMC Med Res Methodol, 11, 96. https://doi.org/10.1186/1471-2288-11-96
  5. Arano I, Sugimoto T, Hamasaki T, et al (2010). Practical application of cure mixture model for long-term censored survivor data from a withdrawal clinical trial of patients with major depressive disorder. BMC Med Res Methodol, 10, 33. https://doi.org/10.1186/1471-2288-10-33
  6. Asano J, Hirakawa A, Hamada C (2014). Assessing the prediction accuracy of cure in the Cox proportional hazards cure model: an application to breast cancer data. Pharmaceutical statistics, 13, 357-63. https://doi.org/10.1002/pst.1630
  7. Boag JW (1949). Maximum likelihood estimates of the proportion of patients cured by cancer therapy. Journal of the Royal Statistical Society. Series B (Methodological), 11, 15-53.
  8. Borges P, Rodrigues J, Louzada F, et al (2012). A cure rate survival model under a hybrid latent activation scheme. Statistical methods in medical research, 0962280212469682.
  9. Chen M-H, Ibrahim JG, Sinha D (1999). A new Bayesian model for survival data with a surviving fraction. Journal of the American Statistical Association, 94, 909-19. https://doi.org/10.1080/01621459.1999.10474196
  10. Corbiere F, Joly P (2007). A SAS macro for parametric and semiparametric mixture cure models. Computer Methods And Programs In Biomedicine, 85, 173-80. https://doi.org/10.1016/j.cmpb.2006.10.008
  11. Cox DR (1972). Regression models and life tables (with discussion). journal of the royal statistical society, series b, 34, 187-220.
  12. Jafari-Koshki T, Mansourian M, Mokarian F (2014). Exploring factors related to metastasis free survival in breast cancer patients using bayesian cure models. Asian Pac J Cancer Prev, 15, 9673. https://doi.org/10.7314/APJCP.2014.15.22.9673
  13. Kim S, Zeng D, Li Y, et al (2013). Joint Modeling of longitudinal and cure-survival data. J Statistical theory and Practice, 7, 324-44. https://doi.org/10.1080/15598608.2013.772036
  14. Lambert PC (2007). Modeling of the cure fraction in survival studies. Stata Journal, 7, 351.
  15. Lambert PC, Thompson JR, Weston CL, et al (2007). Estimating and modeling the cure fraction in population-based cancer survival analysis. Biostatistics, 8, 576-94. https://doi.org/10.1093/biostatistics/kxl030
  16. Maller RA, Zhou X 1996. Survival Analysis with Long-Term Survivors, Wiley.
  17. Ortega EM, Barriga GD, Hashimoto EM, et al (2014). A new class of survival regression models with cure fraction. J Data Science, 12, 107-36.
  18. Ortega EM, Cancho VG, Lachos VH (2008). Assessing influence in survival data with a cure fraction and covariates.
  19. Othus M, Barlogie B, LeBlanc ML, et al (2012). Cure models as a useful statistical tool for analyzing survival. Clinical Cancer Research, 18, 3731-6. https://doi.org/10.1158/1078-0432.CCR-11-2859
  20. Rahimzadeh M, Baghestani AR, Gohari MR, et al (2014). Estimation of the cure rate in Iranian breast cancer patients. Asian Pac J Cancer Prev, 15, 4839-42. https://doi.org/10.7314/APJCP.2014.15.12.4839
  21. Rama R, Swaminathan R, Venkatesan P (2010). Cure models for estimating hospital-based breast cancer survival. Asian Pac J Cancer Prev, 11, 387-91.
  22. Rondeau V, Schaffner E, Corbière F, et al (2013). Cure frailty models for survival data: Application to recurrences for breast cancer and to hospital readmissions for colorectal cancer. Statistical Methods in Medical Research, 22, 243-60. https://doi.org/10.1177/0962280210395521
  23. Sadjadi A, Nouraie M, Mohagheghi Mohammad A, et al (2005). Cancer occurrence in Iran in 2002, an international perspective. Asian Pac J Cancer Prev, 6, 359.
  24. Schmidt P, Witte AD (1989). Predicting criminal recidivism using 'split population' survival time models. J Econometrics, 40, 141-59. https://doi.org/10.1016/0304-4076(89)90034-1
  25. Sposto R (2002). Cure model analysis in cancer: an application to data from the Children's Cancer Group. Statistics in Medicine, 21, 293-312. https://doi.org/10.1002/sim.987
  26. Taghavi A, Fazeli Z, Vahedi M, et al (2012). Increased trend of breast cancer mortality in Iran. Asian Pac J Cancer Prev, 13, 367-70. https://doi.org/10.7314/APJCP.2012.13.1.367
  27. Tournoud M, Ecochard R (2008). Promotion time models with time changing exposure and heterogeneity: application to infectious diseases. Biometrical Journal, 50, 395-407. https://doi.org/10.1002/bimj.200710405
  28. Tsodikov A, Ibrahim J, Yakovlev A (2003). Estimating cure rates from survival data: An alternative to two-component mixture models. J Am Statistical Association, 98.
  29. Yakovlev AY, Tsodikov AD, Asselain B 1996. Stochastic models of tumor latency and their biostatistical applications, OECD Publishing.
  30. Yu B (2008). A frailty mixture cure model with application to hospital readmission cata. Biometrical J, 50, 386-94. https://doi.org/10.1002/bimj.200710399
  31. Yu X, De Angelis R, Andersson TM, et al (2013). Estimating the proportion cured of cancer: some practical advice for users. Cancer Epidemiol, 37, 836-42. https://doi.org/10.1016/j.canep.2013.08.014

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