Cure Models in Survival Analysis

Literature Cited

1. Beran R. 1981. Nonparametric regression with randomly censored survival data Tech. Rep., Univ. Calif Berkeley:
2. Berkson J, Gage RP. 1952. Survival curve for cancer patients following treatment. J. Am. Stat. Assoc. 47:501–15 [Google Scholar]
3. Bertrand A, Legrand C, Carroll R, de Meester C, Van Keilegom I. 2017.a Inference in a survival cure model with mismeasured covariates using a SIMEX approach. Biometrika 104:31–50 [Google Scholar]
4. Bertrand A, Legrand C, Léonard D, Van Keilegom I. 2017.b Robustness of estimation methods in a survival cure model with mismeasured covariates. Comput. Stat. Data Anal. 113:3–18 [Google Scholar]
5. Boag JW. 1949. Maximum likelihood estimates of the proportion of patients cured by cancer therapy. J. R. Stat. Soc. B 11:15–53 [Google Scholar]
6. Bremhorst V, Lambert P. 2016. Flexible estimation in cure survival models using Bayesian P-splines. Comput. Stat. Data Anal. 93:270–84 [Google Scholar]
7. Breslow NE. 1974. Covariates analysis of censored survival data. Biometrics 30:89–99 [Google Scholar]
8. Cai C, Zou Y, Peng P, Zhang J. 2012. smcure: fit semiparametric mixture cure models. R package for estimating semiparametric PH and AFT mixture cure models https://CRAN.R-project.org/package=smcure
9. Carroll R, Ruppert D, Stefanski L, Crainiceanu C. 2006. Measurement Error in Nonlinear Models: A Modern Perspective Boca Raton, FL: Chapman and Hall/CRC. , 2nd ed..
10. Chen MH, Ibrahim JG, Sinha D. 1999. A new Bayesian model for survival data with a surviving fraction. J. Am. Stat. Assoc. 94:909–19 [Google Scholar]
11. Cook JR, Stefanski LA. 1994. Simulation-extrapolation in parametric measurement error models. J. Am. Stat. Assoc. 89:1314–28 [Google Scholar]
12. Cooner F, Banerjee S, Carlin BP, Sinha D. 2007. Flexible cure rate modeling under latent activation schemes. J. Am. Stat. Assoc. 102:560–72 [Google Scholar]
13. Cooner FW, Yu X, Banerjee S, Grambsch PL, McBean AM. 2009. Hierarchical dynamic time-to-event models for post-treatment preventive care data on breast cancer survivors. Stat. Model. 9:119–35 [Google Scholar]
14. Corbière F, Commenges D, Taylor JMG, Joly P. 2009. A penalized likelihood approach for mixture cure models. Stat. Med. 28:510–24 [Google Scholar]
15. Cox DR. 1972. Regression models and life-tables. J. R. Stat. Soc. B 34:187–220 [Google Scholar]
16. de Castro M Cancho VG, Rodrigues J. 2009. A Bayesian long-term survival model parametrized in the cured fraction. Biom. J. 3:443–55 [Google Scholar]
17. Dempster AP, Laird NM, Rubin DB. 1977. Maximum likelihood from incomplete data via EM algorithm. J. R. Stat. Soc. B 39:1–38 [Google Scholar]
18. Diao G, Yin G. 2012. A general transformation class of semiparametric cure rate frailty models. Ann. Inst. Stat. Math. 64:959–89 [Google Scholar]
19. Dirick L, Claeskens G, Baesens B. 2015. An Akaike information criterion for multiple event mixture cure models. Europ. J. Oper. Res. 241:449–57 [Google Scholar]
20. Eilers PHC, Marx BD. 1996. Flexible smoothing B-splines and penalties (with discussion). Stat. Sci. 1:89–121 [Google Scholar]
21. Fan J, Li R. 2001. Variable selection via nonconcave penalized likelihood and its oracle properties. J. Am. Stat. Assoc. 96:1348–60 [Google Scholar]
22. Farewell VT. 1977. A model for binary variable with time-censored observations. Biometrika 64:43–46 [Google Scholar]
23. Farewell VT. 1982. The use of a mixture model for the analysis of survival data with long-term survivors. Biometrics 38:1041–46 [Google Scholar]
24. Farewell VT, Long DL, Tom BDM, Yiu S, Su L. 2017. Two-part and related regression models for longitudinal data. Annu. Rev. Stat. Appl. 4:283–315 [Google Scholar]
25. Gelfand AE, Ghosh SK. 1998. Model choice: a minimum posteriori predictive loss approach. Biometrika 85:1–11 [Google Scholar]
26. Ghitany ME, Maller RA, Zhou S. 1994. Exponential mixture models with long-term survivors and covariates. J. Multivar. Anal. 49:218–41 [Google Scholar]
27. Hanin L, Huang L-S. 2014. Identifiability of cure models revisited. J. Multivar. Anal. 130:261–74 [Google Scholar]
28. Hsu WW, Todem D, Kim KM. 2016. A sup-score test for the cure fraction in mixture models for long-term survivors. Biometrics 76:1348–57 [Google Scholar]
29. Ibrahim JG, Chen MH, Sinha D. 2001. Bayesian semiparametric models for survival data with a cure fraction. Biometrics 57:383–88 [Google Scholar]
30. Kalbfleisch JD, Prentice RL. 1973. Marginal likelihoods based on Cox's regression and life model. Biometrika 60:267–78 [Google Scholar]
31. Kaplan EL, Meier P. 1958. Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53:457–81 [Google Scholar]
32. Kim S, Chen MH, Dey DK, Gamerman D. 2007. Bayesian dynamic models for survival data with a cure fraction. Lifetime Data Anal 13:17–35 [Google Scholar]
33. Kim S, Chen MH, Dey DK. 2011. A new threshold regression model for survival data with a cure fraction. Lifetime Data Anal 17:101–22 [Google Scholar]
34. Kuk AYC, Chen CH. 1992. A mixture model combining logistic regression with proportional hazards regression. Biometrika 79:531–41 [Google Scholar]
35. Lam KF, Fong DYT, Tang OY. 2005. Estimating the proportion of cured patients in censored sample. Stat. Med. 24:1865–79 [Google Scholar]
36. Lang S, Brezger A. 2004. Bayesian P-splines. J. Comput. Graph. Stat. 13:183–212 [Google Scholar]
37. Laud P, Ibrahim J. 1995. Predictive model selection. J. R. Stat. Soc. B 57:247–62 [Google Scholar]
38. Li SC, Taylor JMG. 2002. A semi-parametric accelerated failure time cure model. Stat. Med. 21:3235–47 [Google Scholar]
39. López-Cheda A, Cao R, Jácome MA, Van Keilegom I. 2017. Nonparametric incidence estimation and bootstrap bandwidth selection in mixture cure models. Comp. Stat. Data Anal. 105:144–65 [Google Scholar]
40. Liu X, Peng Y, Tu D, Liang H. 2012. Variable selection in semiparametric cure models based on penalized likelihood, with application to breast cancer clinical trials. Stat. Med. 31:2882–91 [Google Scholar]
41. Lu W. 2008. Maximum likelihood estimation in the proportional hazards cure model. Ann. Inst. Stat. Math. 60:545–74 [Google Scholar]
42. Lu W. 2010. Efficient estimation for an accelerated failure time model with a cure fraction. Stat. Sin. 20:661–74 [Google Scholar]
43. Lu W, Ying Z. 2004. On semiparametric transformation cure models. Biometrika 91:331–43 [Google Scholar]
44. Ma Y, Yin G. 2008. Cure rate model with mismeasured covariates under transformation. J. Am. Stat. Assoc. 103:743–56 [Google Scholar]
45. Maller RA, Zhou S. 1996. Survival Analysis with Long Term Survivors New York: Wiley
46. Meeker WQ. 1987. Limited failure population life tests: Application to integrated circuit reliability. Technometrics 29:51–65 [Google Scholar]
47. Mizoi MF, Bolfarine H, Pedroso-De-Lima AC. 2007. Cure rate model with measurement error. Comm. Stat. Simul. Comput. 36:185–96 [Google Scholar]
48. Moeschberger ML, Klein JP. 1985. A comparison of several methods of estimating the survival function when there is extreme right censoring. Biometrics 41:253–59 [Google Scholar]
49. Patilea V, Van Keilegom I. 2017. A general approach for cure models in survival analysis. arXiv1701.03769 [math.ST]
50. Peng Y. 2003.a Estimating baseline distribution in proportional hazards cure models. Comp. Stat. Data Anal. 42:187–201 [Google Scholar]
51. Peng Y. 2003.b Fitting semiparametric cure models. Comput. Stat. Data Anal. 41:481–90 [Google Scholar]
52. Peng Y, Dear KBG. 2000. A nonparametric mixture model for cure rate estimation. Biometrics 56:237–43 [Google Scholar]
53. Peng Y, Dear KBG, Denham JW. 1998. A generalized F mixture model for cure rate estimation. Stat. Med. 17:813–30 [Google Scholar]
54. Peng Y, Taylor JMG. 2014. Cure models. Handbook of Survival Analysis J Klein, H van Houwelingen, JG Ibrahim, TH Scheike 113–34 Boca Raton, FL: Chapman and Hall [Google Scholar]
55. Peng Y, Taylor JMG. 2017. Residual-based model diagnosis methods for mixture cure models. Biometrics 73:495–505 [Google Scholar]
56. Peng Y, Xu J. 2012. An extended cure model and model selection. Lifetime Data Anal 18:215–33 [Google Scholar]
57. Portier F, El Ghouch A, Van Keilegom I. 2017. Efficiency and bootstrap in the promotion time cure model. Bernoulli 23:3437–68 [Google Scholar]
58. Prentice RL. 1974. A log-gamma model and its maximum likelihood estimation. Biometrika 61:538–44 [Google Scholar]
59. Ritov Y. 1990. Estimation in a linear regression model with censored data. Ann. Stat. 18:303–28 [Google Scholar]
60. Rodrigues J, Canch VG, de Castro M, Louzada-Neto F. 2009. On the unification of long-term survival models. Stat. Probab. Lett. 79:753–59 [Google Scholar]
61. Schmidt P, Witte AD. 1989. Predicting criminal recidivism using split population survival time models. J. Econom. 40:141–59 [Google Scholar]
62. Scolas S, El Ghouch A, Legrand C, Oulhaj A. 2016. Variable selection in a flexible parametric mixture cure model with interval-censored data. Stat. Med. 35:1210–25 [Google Scholar]
63. Sy JP, Taylor JMG. 2000. Estimation of a Cox proportional hazards cure model. Biometrics 56:227–36 [Google Scholar]
64. Taylor JMG. 1995. Semi-parametric estimation in failure time mixture models. Biometrics 51:899–907 [Google Scholar]
65. Taylor JMG, Liu N. 2007. Statistical issues involved with extending standards models. Advances in Statistical Modeling and Inference: Essays in Honor of Kjell A Doksum V Nair 299–311 Singapore: World Sci. [Google Scholar]
66. Tournoud M, Ecochard R. 2008. Promotion time models with time-changing exposure and heterogeneity: application to infectious diseases. Biom. J. 3:395–407 [Google Scholar]
67. Tsodikov A. 1998.a A proportional hazards model taking account of long-term survivors. Biometrics 54:1508–16 [Google Scholar]
68. Tsodikov A. 1998.b Asymptotic efficiency of proportional hazards model with cure. Stat. Probab. Lett. 39:237–44 [Google Scholar]
69. Tsodikov A. 2001. Estimation of survival based on proportional hazards when cure is a possibility. Math. Comput. Model. 33:1227–36 [Google Scholar]
70. Tsodikov A. 2002. Semi-parametric models of long- and short-term survival: an application to the analysis of breast cancer survival in Utah by age and stage. Stat. Med. 21:895–920 [Google Scholar]
71. Tsodikov AD, Ibrahim JG, Yakovlev AY. 2003. Estimating cure rates from survival data. J. Am. Stat. Assoc. 98:1063–78 [Google Scholar]
72. Wang L, Pang D, Liang H. 2012. Two-component mixture cure rate model with spline estimated nonparametric components. Biometrics 68:726–35 [Google Scholar]
73. Wang Y, Klijn JGM, Sieuwerts AM, Look MP, Yang F. et al. 2005. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365:671–79 [Google Scholar]
74. Wei LJ. 1992. The accelerated failure time model: a useful alternative to the Cox regression model in survival analysis. Stat. Med. 11:1871–79 [Google Scholar]
75. Wileyto EP, Li Y, Chen J, Heitjan DF. 2013. Assessing the fit of parametric cure models. Biostatistics 14:340–50 [Google Scholar]
76. Xu J, Peng Y. 2014. Nonparametric cure rate estimation with covariates. Can. J. Stat. 42:1–17 [Google Scholar]
77. Yakovlev AY, Asselain B, Bardou VJ, Fouquet A, Hoang T. et al. 1993. A simple stochastic model of tumor recurrence and its application to data on premenopausal breast cancer. Biom. Anal. Données Spatio-temporelles 12:66–83 [Google Scholar]
78. Yakovlev AY, Tsodikov AD, Asselain B. 1996. Stochastic Models of Tumor Latency and Their Biostatistical Applications Singapore: World Sci.
79. Yamaguchi K. 1992. Accelerated failure-time regression models with a regression model of surviving fraction: an application to the analysis of “permanent employment” in Japan. J. Am. Stat. Assoc. 87:284–92 [Google Scholar]
80. Yin G, Ibrahim JG. 2005. Cure rate model: a unified approach. Can. J. Stat. 33:559–70 [Google Scholar]
81. Zhao Y, Lee AH, Yau KKW, Burke V, McLachlan GJ. 2009. A score test for assessing the cured proportion in long-term survivor mixture model. Stat. Med. 28:3454–66 [Google Scholar]
82. Zhang J, Peng Y. 2007. A new estimation method for the semiparametric accelerated failure time mixture cure model. Stat. Med. 26:3157–71 [Google Scholar]
83. Zeng D, Yin G, Ibrahim JG. 2006. Semiparametric transformation for survival data with a cure fraction. J. Am. Stat. Assoc. 101:670–84 [Google Scholar]