Erosion of regression effect in a survival study

Biometrics

Volumn 60, Issue 2, 2004, Pages 344-351

  O'Quigley, John ^a   Natarajan, Loki ^b  

^a INSTITUT CURIE   (France)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Changepoint models; Cox model; Estimating equation; Kaplan Meier estimate; Partial likelihood; Stochastic processes; Time varying effects; Weighted score

Indexed keywords

HAZARDS; MEDICAL APPLICATIONS; RANDOM PROCESSES; REGRESSION ANALYSIS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

CHANGE-POINT MODELS; COX MODEL; ESTIMATING EQUATIONS; KAPLAN-MEIER ESTIMATES; MOST LIKELY; PARTIAL LIKELIHOODS; PROPORTIONAL HAZARDS; REGRESSION EFFECTS; TIME-VARYING EFFECTS; WEIGHTED SCORE;

EROSION;

RISK;

ARTICLE; BREAST CANCER; CANCER SURVIVAL; HUMAN; HYPOTHESIS; KAPLAN MEIER METHOD; NONBIOLOGICAL MODEL; PRACTICE GUIDELINE; PROGNOSIS; REGRESSION ANALYSIS; RISK ASSESSMENT;

EID: 2942668685     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.0006-341X.2004.00178.x     Document Type: Article

References (41)

1. Andersen, P. K. and Gill, R. D. (1982). Cox's regression model for counting processes: A large sample study. Annals of Statistics 10, 1100-1120.
2. Anderson, J. A. and Senthilselvan, A. (1980). Smooth estimates of the hazard function. Journal of the Royal Statistical Society, Series B 42, 322-327.
3. Anderson, J. A. and Senthilselvan, A. (1982). A two-step regression model for hazard functions. Applied Statistics 31, 44-51.
4. Bartle, R. (1976). Elements of Real Analysis. New York: Wiley.
5. Breslow, N. E. (1972). Contribution to discussion of paper by D.R. Cox. Journal of the Royal Statistical Society, Series B 34, 216-217.
6. Breslow, N. E. (1974). Covariance analysis of censored survival data. Biometrics 30, 89-100.
7. Bretagnolle, J. and Huber-Carol, C. (1988). Effects of omitting covariates in Cox's model for survival data. Scandinavian Journal of Statistics 15, 125-138.
8. Cox, D. R. (1972). Regression models and life tables (with discussion). Journal of the Royal Statistical Society, Series B 34, 187-215.
9. Cox, D. R. and Oakes, D. (1984). Analysis of Survival Data. London: Chapman and Hall.
10. Davies, R. B. (1977). Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika 64, 247-254.
11. Davies, R. B. (1987). Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika 74, 33-43.
12. Efron, B. (1981a). Censored data and the bootstrap. Journal of the American Statistical Association 76, 312-319.
13. Efron, B. (1981b). Nonparametric estimates of standard error: The jackknife, the bootstrap and other resampling methods. Biometrika 68, 589-599.
14. Fleming, T. R. and Harrington, D. P. (1991). Counting Processes and Survival Analysis. New York: Wiley.
15. Ford, I., Norrie, J., and Ahmadi, S. (1995). Model inconsistency, illustrated by the Cox proportional hazards model. Statistics in Medicine 14, 735-746.
16. Gill, R. D. and Schumacher, M. (1987). A simple test of the proportional hazards assumption. Biometrika 74, 289-300.
17. Gore, S. M., Pocock, S. J., and Kerr, G. R. (1984). Regression models and nonproportional hazards of the analysis of breast cancer survival. Applied Statistics 33, 176-196.
18. Gray, R. J. (1992). Flexible methods for analyzing survival data using splines with application to breast cancer prognosis. Journal of the American Statistical Association 87, 942-951.
19. Harrington, D. P., Fleming, T. R., and Green, S. J. (1982). Procedures for serial testing in censored survival data. In Institute of Mathematical Statistics Lecture Notes - Monograph Series: Survival Analysis, J. Crowley and R. Johnson (eds), 269-286.
20. Hastie, T. and Tibshirani R. (1993). Varying-coefficient models. Journal of the Royal Statistical Society, Series B 55, 757-796.
21. Kalbfleisch, J. and Prentice, R. L. (1980). The Statistical Analysis of Failure Time Data. New York: Wiley.
22. Klotz, J. (1982). Spline smooth estimates of survival. In Institute of Mathematical Statistics Lecture Notes-Monograph Series: Survival Analysis, J. Crowley and R. Johnson (eds), 14-25.
23. Lausen, B. and Schumacher, M. (1996). Evaluating the effect of optimized cutoff values in the assessment of prognostic factors. Computational Statistics and Data Analysis 21, 307-326.
24. Lawless, J. F. (1982). Statistical Models and Methods for Life-time Data. New York: Wiley.
25. Liang, K. Y., Self, S. G., and Liu, X. (1990). The Cox proportional hazards model with change point: An epidemiological application. Biometrics 46, 783-793.
26. Marzec, L. and Marzec, P. (1997). On fitting Cox's regression model with time-dependent coefficients. Biometrika 84, 901-908.
27. Moreau, T., O'Quigley, J., and Mesbah, M. (1985). A global goodness of fit statistic for the proportional hazards model. Applied Statistics 34, 212-218.
28. Murphy, S. A. and Sen, P. K. (1991). Time-dependent coefficients in a Cox-type regression model. Stochastic Processes and Their Applications 39, 153-180.
29. Nagelkerke, N. J. D., Oosting, J., and Hart, A. A. M. (1984). A simple test of goodness of fit of Cox's proportional hazards model. Biometrics 40, 483-486.
30. O'Quigley, J. (2003). Khmaladze type graphical evaluation of the proportional hazards assumption. Biometrika 90, 577-584.
31. O'Quigley, J. and Pessione, F. (1989). Score tests for homogeneity of regression effect in the proportional hazards model. Biometrics 45, 135-144.
32. O'Quigley, J. and Pessione, F. (1991). The problem of a covariate-time qualitative interaction in a survival study. Biometrics 47, 101-115.
33. Sather, H., Coccia, P., Nesbit, M., Level, C., and Hammond, D. (1981). Disappearance of the predictive value of prognostic variables in childhood acute lymphoblastic leukemia. Cancer 48, 370-376.
34. Schumacher, M. and Vaeth, M. (1984). On a goodness of fit test for the proportional hazards model. EDP in Biology and Medicine 15, 19-23.
35. Serfling, R. (1980). Approximation Theorems of Mathematical Statistics. New York: Wiley.
36. Struthers, C. A. and Kalbfleisch, J. D. (1986). Misspecified proportional hazards models. Biometrika 73, 363-369.
37. Tsiatis, A. (1982). Group sequential methods for survival analysis with staggered entry. In Institute of Mathematical Statistics Lecture Notes-Monograph Series: Survival Analysis, J. Crowley and R. Johnson (eds), 257-268.
38. Verweij, J. A. and Van Houwelingen, H. A. (1995). Time-dependent effects of fixed covariates in Cox regression. Biometrics 51, 1550-1556.
39. Wei, L. J. (1984). Testing goodness of fit for proportional hazards model with censored observations. Journal of the American Statistical Association 79, 649-652.
40. Whittemore, A. S. and Keller, J. B. (1986). Survival estimation using splines. Biometrics 42, 495-506.
41. Zucker, D. M. and Karr, A. F. (1990). Nonparametric survival analysis with time-dependent covariate effects: A penalized partial likelihood approach. Annals of Statistics 18, 329-353.