Estimating the personal cure rate of cancer patients using population-based grouped cancer survival data

Statistical Methods in Medical Research

Volumn 20, Issue 3, 2011, Pages 261-274

  Yu, Binbing ^a   Tiwari, Ram C ^b   Feuer, Eric J ^c  

^a NATIONAL INSTITUTE ON AGING   (United States)

^b CENTER FOR DRUG EVALUATION AND RESEARCH   (United States)

^c NATIONAL CANCER INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGED; ARTICLE; CANCER PATIENT; CANCER STAGING; CANCER SURVIVAL; CAUSE OF DEATH; COLORECTAL CANCER; DATA ANALYSIS; HEART DISEASE; HUMAN; NONPARAMETRIC TEST; POPULATION; PROBABILITY; RACE; RISK ASSESSMENT; STATISTICAL MODEL;

AGED; AGED, 80 AND OVER; CAUSE OF DEATH; COLORECTAL NEOPLASMS; DATA INTERPRETATION, STATISTICAL; FEMALE; HUMANS; KAPLAN-MEIER ESTIMATE; MALE; MIDDLE AGED; MODELS, STATISTICAL; NEOPLASMS; PROPORTIONAL HAZARDS MODELS; REGISTRIES; RISK FACTORS; SEER PROGRAM; SURVIVAL ANALYSIS; UNITED STATES;

EID: 80051903368     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280209347046     Document Type: Article

References (17)

1. Cronin A, Feuer EJ. Cumulative cause-specific mortality for cancer patients in the presence of other causes: a crude analogue of relative survival. Statistics in Medicine 2000; 19: 1729-40. (Pubitemid 30436059)
2. Shairer C, Mink PJ, Carroll L, Devesa SS. Probabilities of death from breast cancer and other causes among female breast cancer patients. Journal of the National Cancer Institute 2004; 96: 1311-21. (Pubitemid 39264120)
3. Gordon NH. Application of the theory of finite mixtures for the estimation of 'cure' rates of treated cancer patients. Statistics in Medicine 1990; 9: 397-407. (Pubitemid 20245411)
4. Kalbfleisch JD, Prentice RL. The statistical analysis of failure time data (2nd edn). Wiley, New York; 2002.
5. David HA, Moeschberger ML. The theory of competing risks. Griffin, London; 1978.
6. Gail M. A review and critique of some models used in competing risk analysis. Biometrics 1975; 31: 209-22.
7. Larson MG, Dinse GE. A mixture model for the regression analysis of competing risks data. Applied Statistics 1985; 34: 201-11.
8. Cutler SJ, Ederer F. Maximum utilization of the life table method in analyzing survival. Journal of Chronical Disease 1958; 8: 699-712.
9. Chiang CL. Competing risks in mortality analysis. Annual Review of Public Health 1991; 12: 281-307.
10. Finkelstein DM. A proportional hazards model for interval-censored failure time data. Biometrics 1986; 42: 845-54. (Pubitemid 17134903)
11. Pan W, Chappell R. Estimation in the Cox proportional hazards model with left-truncated and interval-censored data. Biometrics 2002; 58: 64-70. (Pubitemid 34194489)
12. Goetghebeur E, Ryan L. Semiparametric regression analysis of interval-censored data. Biometrics 2000; 56: 1139-44.
13. Prentice RL, Gloeckler LA. Regression analysis of grouped survival data with application to breast cancer data. Biometrics 1978; 34: 57-67. (Pubitemid 8316897)
14. Louis TA. Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society Series B 1982; 44: 226-33.
15. Meng XL, Rubin DB. Using EM to obtain asymptotic variance-covariance matrices: the SEM algorithm. Journal of the American Statistical Association 1991; 86: 899-909.
16. Rubin DB, Schenker N. Multiple imputation in health-care data bases: an overview and some applications. Statistics in Medicine 1991; 10: 585-98.
17. Gaynor JJ, Feuer EJ, Tan CC et al. On the use of cause-specific failure and conditional failure probabilities: examples from clinical oncology data. Journal of the American Statistical Association 1993; 88: 400-9.