Parametric models for accelerated and long-term survival: A comment on proportional hazards

Statistics in Medicine

Volumn 21, Issue 21, 2002, Pages 3279-3289

  Frankel, Paul ^a   Longmate, Jeffrey ^a  

^a CITY OF HOPE NATIONAL MEDICAL CENTER   (United States)

Author keywords

Accelerated failure; Cure model; Long normal; Proportional hazards

Indexed keywords

ACUTE GRANULOCYTIC LEUKEMIA; AGE DISTRIBUTION; ALGORITHM; ARTICLE; BONE MARROW TRANSPLANTATION; CALCULATION; CANCER SURVIVAL; CLINICAL TRIAL; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; COVARIANCE; DIAGNOSTIC PROCEDURE; FOLLOW UP; HUMAN; MAJOR CLINICAL STUDY; NONBIOLOGICAL MODEL; SURVIVAL RATE; TREATMENT FAILURE;

ALGORITHMS; BONE MARROW TRANSPLANTATION; HUMANS; LEUKEMIA, MYELOCYTIC, ACUTE; PROPORTIONAL HAZARDS MODELS; SURVIVAL ANALYSIS;

EID: 0037110577     PISSN: 02776715     EISSN: None     Source Type: Journal    
DOI: 10.1002/sim.1273     Document Type: Article

References (42)

1. Cox DR. Regression models and life tables (with discussion). Journal of the Royal Statistical Society, Series B 1972; 24:187-200.
2. Garfield E. 100 most cited papers of all time. Current Contents 1990; Feb. 12.
3. Boag JW. The presentation and analysis of the results of radiotherapy. British Journal of Radiology 1948; 21 Pt I: 128, Pt II: 189.
4. Boag JW. Maximum likelihood estimates of the proportion of patients cured by cancer therapy. Journal of the Royal Statistical Society, Series B 1949; 11:15-50.
5. Gamel JW, Vogel RL, Valagussa P, Bonadonna G. Parametric survival analysis of adjuvant therapy for stage 11 breast cancer. Cancer 1994; 74:2483-2490.
6. Garnel JW, McLean IW. A stable, multivariate extension of the log-normal survival model. Computers and Biomedical Research 1994; 27:148-155.
7. Sposto R. Cure model analysis in cancer: an application to data from the Children's Cancer Group. Statistics in Medicine 2002; 21:293-312.
8. Gamel JW, McLean IW, McCurdy JB. Biologic distinctions between cure and time to death in 2892 patients with intraocular melanoma. Cancer 1993; 71:22299-22305.
9. Gamel JW, Jones AS. Squamous carcinoma of the head and neck: cured fraction and median survival time as functions of age, sex, histologic type, and node status. British Journal of Cancer 1993; 67:1071-1075.
10. Gamel JW, George SL, Stanley WE, Seigler HF. Skin melanoma: cured fraction and survival time as functions of thickness, site, histologic type, age and sex. Cancer 1993; 72:1219-1223.
11. Gamel JW, Vogel RL. A model of long-term survival following adjuvant therapy for stage 2 breast cancer. British Journal of Cancer 1993; 68:1167-1170.
12. Gamel JW, Vogel RL, McLean IW. Assessing the impact of adjuvant therapy on cure rate for stage 2 breast carcinoma. British Journal of Cancer 1993; 68:115-118.
13. Gamel JW, Meyer JS, Province MA. Proliferative rate by S-phase measurement may affect cure of breast carcinoma. Cancer 1995; 76(6):1010-1018.
14. Edwards MJ, Gamel JW, Feuer EJ. Improvement in the prognosis of Breast cancer from 1965 to 1984. Journal of Clinical Oncology 1998; 16(3):1030-1035.
15. Edwards MJ, Garnel JW, Vaughan WP, Wrightson WR. Infiltrating ductal carcinoma of the breast: the survival impact of race. Journal of Clinical Oncology 1998; 16(8):2693-2699.
16. Cantor A, Shuster JJ. Parametric versus non-parametric methods for estimating cure rates based on censored survival data. Statistics in Medicine 1992; 11:931-937.
17. Sposto R, Sather HN. Determining the duration of comparative clinical trials while allowing for cure. Journal of Chronic Disease 1985; 38(8):683-690.
18. Sposto R, Sather HN, Baker SA. A comparison of tests of difference in proportion of patients who are cured. Biometrics 1992; 48:87-99.
19. Goldman A. Survivorship analysis when cure is a possibility: a Monte Carlo study. Statistics in Medicine 1984; 3:153-163.
20. Farewell VT. The use of mixture models for the analysis of survival data with long-term survivors. Biometrics 1982; 38:1041-1046.
21. Farewell VT. A model for a binary variable with time-censored observations. Biometrika 1977; 64:43-46.
22. Yamaguchi K. Accelerated failure-time regression models with a regression model of surviving fraction: an application to the analysis of 'permanent employment' in Japan. Journal of the American Statistical Association 1992; 87:284-292.
23. Gaddurn JH. Lognormal distributions. Nature 1945;156:463.
24. McCready DR, Chapman JA, Hanna WM, Kahn HJ, Murray D, Fish EB, Trudeau ME, Andrulis IL, Lickley HL. Factors affecting distant disease-free survival for primary invasive breast cancer: use of a log-normal survival model. Annals of'Surgical Oncology 2000; 7(6):416-426.
25. Berg JW. The distribution of cancer deaths in time. A survey test of the lognormal model. British Journal of Cancer 1965; 19:695-711.
26. Venables WN, Ripley BD. MASS library: (part of S-plus distribution), MASSσtats.ox.ac.uk, 1999 (also presented Modern Applied Statistics with S-plus. Springer-Verlag, 1994).
27. Feuer EJ, Kessler LG, Baker SG, Triolo HE, Green DT. The impact of breakthrough clinical trials on survival in population based tumor registries. Journal of Clinical Epidemiology 1991; 44:141-153.
28. Gamel JW, Vogel RL. Comparison of parametric and non-parametric survival methods using simulated clinical data. Statistics in Medicine 1997; 16:1629-1643.
29. Cox DR, Oakes D. Analysis of survival data. Monographs on Statistics and Applied Probability. Chapman-Hall, 1994; 21.
30. Allison PD. Survival Analysis Using the SAS System: A Practical Guide. SAS Institute: Cary, N.C., 1997.
31. Cantor A. Extending SAS Survival Analysis: Techniques for Medical Research. SAS Institute: Cary, N.C., 1997.
32. SAS/STAT User's Guide, Version 8, Volume 3. SAS Institute Inc.: Cary, N.C., 1999; 2576.
33. Gill R, Schuacher M. A simple test of the proportional hazards assumption. Biometrika 1987; 74:289-300.
34. Garnet JW, Jones AS. Squamous carcinoma of the head and neck: cured fraction and median survival time as functions of age, sex, histologic type, and node status. British Journal of Cancer 1993; 67:1071-1075.
35. D'Agostino RB. Editorial: controlling alpha in a clinical trial: the case for secondary endpoints. Statistics in Medicine 2000; 19:763-766.
36. Moyé LA. Alpha calculus in clinical trials: considerations and commentary for the new millennium. Statistics in Medicine 2000; 19:767-779.
37. Koch GG. Discussion for 'Alpha calculus in clinical trials: considerations and commentary for the new millennium'. Statistics in Medicine 2000;19:781-785.
38. Pepe MS. Fleming TR. Weighted Kaplan-Meier statistics: a class of distance tests for censored survival data. Biometrics 1989;45:497-507.
39. Fleming TR, Harrington DP, O'Sullivan, M. Supremum versions of the log-rank and generalized Wilcoxon statistics. Journal of the American Statistical Association 1987; 82 397:312-320.
40. Harrington DP, Fleming TR. A class of rank test procedures for censored survival data. Biometrika 1982; 693:553-566.
41. Breslow NE, Edler L, Berger J. A two-sample censored-data rank for acceleration. Biometrics 1984; 40:1049-1062.
42. Reid N. A conversation with Sir David Cox. Statistical Science 1994; 9(3):439-455.