Additive Hazards Regression with Covariate Measurement Error

Journal of the American Statistical Association

Volumn 95, Issue 449, 2000, Pages 238-248

  Kulich, Michal ^a   Lin, D Y ^b  

^a CHARLES UNIVERSITY   (Czech Republic)

^b UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Censoring; Corrected score; Error in variables; Failure time; Surrogate covariate

Indexed keywords

EID: 0442309549     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1080/01621459.2000.10473917     Document Type: Article

References (17)

1. Andersen, P. K., and Gill, R. D. (1982), “Cox's Regression Model for Counting Processes: A Large-Sample Study," The Annals of Statistics, 10, 1100-1120.
2. Breslow, N. E., and Day, N. E. (1987), Statistical Models in Cancer Research, 2. The Design and Analysis of Cohort Studies, Lyon: I ARC.
3. Carroll, R. J., Ruppert, D., and Stefanski, L. A. (1995). Measurement Error in Nonlinear Models, London: Chapman and Hall.
4. Cox, D. R. (1972), “Regression Models and Life Tables” (with discussion). Journal of the Royal Statistical Society, Ser. B, 34, 187-220.
5. Cox, D. R., and Oakes, D. (1984). Analysis of Survival Data, London: Chapman and Hall.
6. D’Angio, G. J., Breslow, N., Beckwith, J. B., Evans, A., Baum, H., de Lorimier, A., Fembach, D., Hrabovsky, E., Jones, B., and Kelalis, P. (1989), ‘Treatment of Wilms Tumor: Results of the Third National Wilms Tumor Study,” Cancer, 64, 349-360.
7. Fuller, W. A. (1987), Measurement Error Models. New York: Wiley.
8. Green, D. M., Breslow, N. E., Beckwith, J. B., Finklestein, J. Z., Grundy, P. G., Thomas, P. R. M., Kim, T., Shochat, S., Haase, G. M., Ritchey, M. L., Kelalis, P. P., and D’Angio, G. J. (1998). "Comparison Between Single-Dose and Divided-Dose Administration of Dactinomycin and Doxorubicin for Patients With Wilms Tumor. A Report From the National Wilms Tumor Study Group,” Journal of Clinical Oncology, 16, 237-245.
9. Kulich, M. (1997). "Additive Hazards Regression With Incomplete Covari-ate Data,” Ph.D. dissertation, Seattle: University of Washington.
10. Lin, D. Y., and Ying, Z. (1994). "Semiparametric Analysis of the Additive Risk Model." Biometrika, 81,61-71.
11. Nakamura, T. (1992), "Proportional Hazards Model With Covariates Subject to Measurement Error," Biometrics, 48. 829-838.
12. Prentice, R. L. (1982), "Covariate Measurement Errors and Parameter Estimation in a Failure Time Regression Model," Biometrika, 69. 331-342.
13. Shorack, G. R., and Wellner, J. A. (1986). Empirical Processes With Applications to Statistics, New York: Wiley.
14. Stefanski, L. A. (1989). “Unbiased Estimation of a Nonlinear Function of a Normal Mean With Applications to Measurement Error Models,” Communications in Statistics, Part A, 18, 4335-4358.
15. van der Vaart, A. W., and Wellner, J. A. (1996), Weak Convergence and Empirical Processes, New York: Springer-Verlag.
16. Wang, C. Y., Hsu, L., Feng, Z. D., and Prentice, R. L. (1997), “Regression Calibration in Failure Time Regression,” Biometrics, 53, 131-145.
17. Zhou, H., and Pepe, M. S. (1995). “Auxiliary Covariate Data in Failure Time Regression,” Biometrika, 82, 139-149.