Comparing Cox and parametric models in clinical studies

Statistics in Medicine

Volumn 22, Issue 23, 2003, Pages 3597-3610

  Nardi, Alessandra ^a,b   Schemper, Michael ^b  

^a UNIVERSITY OF TERAMO   (Italy)

^b UNIVERSITY OF VIENNA   (Austria)

Author keywords

Goodness of fit; Model choice; Normal deviate residuals; Survival analysis

Indexed keywords

AMINOGLUTETHIMIDE; ESTROGEN RECEPTOR; TAMOXIFEN CITRATE;

ARTICLE; AUSTRIA; BREAST CANCER; CANCER GRADING; CANCER STAGING; CANCER SURVIVAL; CLINICAL STUDY; HEART TRANSPLANTATION; HUMAN; INTERMETHOD COMPARISON; LIVER CANCER; LIVER TRANSPLANTATION; LOGISTIC REGRESSION ANALYSIS; LUNG NON SMALL CELL CANCER; MEDICAL SCHOOL; NORMAL DISTRIBUTION; PARAMETRIC TEST; POSTOPERATIVE INFECTION; PROBABILITY; PROGNOSIS; SAMPLE SIZE; SIMULATION; STATISTICAL ANALYSIS; STATISTICAL MODEL; TREATMENT OUTCOME;

ANTINEOPLASTIC AGENTS; BREAST NEOPLASMS; CARCINOMA, NON-SMALL-CELL LUNG; CLINICAL TRIALS; FEMALE; HEART TRANSPLANTATION; HUMANS; LIVER TRANSPLANTATION; LUNG NEOPLASMS; MALE; PROPORTIONAL HAZARDS MODELS;

EID: 0344035230     PISSN: 02776715     EISSN: None     Source Type: Journal    
DOI: 10.1002/sim.1592     Document Type: Article

References (19)

1. Cox DR. Regression models and life-tables. (with discussion). Journal of the Royal Statistical Society B 1972; 34:187-220.
2. Kalbfleisch JD, Prentice RL. The Statistical Analysis of Failure Time Data. Wiley: New York, 1980.
3. Lawless JF. Parametric models in survival analysis. In Encyclopaedia of Biostatistics, Armitage P, Colton T (eds). Wiley: New York, 1998; 3254-3264.
4. Efron B. The efficiency of Cox's likelihood function for censored data. Journal of the American Statistical Association 1977; 72:557-565.
5. Oakes D. The asymptotic information in censored survival data. Biometrika 1977; 64:441-448.
6. Gelber RD, Goldhirsch A, Cole BF. Parametric extrapolation of survival estimates with applications to quality of life evaluation of treatments. Controlled Clinical Trials 1993; 14:485-499.
7. Nardi A, Schemper M. New residuals for Cox regression and their application to outlier screening. Biometrics 1999; 55:523-529.
8. Muers MF, Shevlin P, Brown J. Prognosis in lung cancer: physicians' opinions compared with outcome and a predictive model. Thorax 1996; 51:894-902.
9. Therneau TM, Grambsch PM. Modeling Survival Data. Extending the Cox Model. Springer: New York, 2000.
10. Cox DR, Snell EJ. A general definition of residuals (with discussion). Journal of the Royal Statistical Society B 1968; 30:248-275.
11. Grambsch PM, Therneau TM, Fleming TR. Diagnostic plots to reveal functional form for covariates in multiplicative intensity models. Biometrics 1995; 51:1469-1482.
12. Schoenfeld D. Partial residuals for the proportional hazards regression model. Biometrika 1982; 69:239-241.
13. Grambsch PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika 1994; 81:515-526.
14. SAS. SAS/STAT User's Guide, Version 8. SAS Institute Inc.: Cary, NC, USA.
15. Schemper M, Smith TL. A note on quantifying follow-up in studies of failure time. Controlled Clinical Trials 1996; 17:343-346.
16. Hess KR. Assessing time-by-covariate interactions in proportional hazard regression models using cubic spline functions. Statistics in Medicine 1994; 13:1045-1062.
17. Heinzl H, Kaider A. Gaining more flexibility in Cox proportional hazards regression models with cubic spline functions. Computer Methods and Programs in Biomedicine 1997; 54:201-208.
18. Cox DR, Oakes D. Analysis of Survival Data. Chapman & Hall: London, 1984.
19. Cox DR. Tests of separate families of hypotheses. Proceedings of the 4th Berkeley Symposium, Vol. 1. University of California Press: Berkeley, U.S.A, 1961; 105-123.