Rate/mean regression for multiple-sequence recurrent event data with missing event category

Scandinavian Journal of Statistics

Volumn 33, Issue 2, 2006, Pages 191-207

  Schaubel, Douglas ^a   Cai, Jianwen ^b  

^a UNIVERSITY OF MICHIGAN   (United States)

^b IL   (United States)

Author keywords

Empirical processes; Missing data; Proportional means model; Semi parametric model; Weighted estimating equations

Indexed keywords

EID: 33745310945     PISSN: 03036898     EISSN: 14679469     Source Type: Journal    
DOI: 10.1111/j.1467-9469.2006.00459.x     Document Type: Article

References (21)

1. Andersen, P. K. & Gill, R. D. (1982). Cox's regression model for counting processes: a large sample study. Ann. Statist. 10, 1100-1120.
2. Bilias, Y., Gu, M. & Ying, Z. (1997). Towards a general asymptotic theory for the Cox model with staggered entry. Ann. Statist. 25, 662-682.
3. Cai, J. & Schaubel, D. E. (2004). Marginal rates/means models for multiple-type recurrent event data. Lifetime Data Anal. 10, 121-138.
4. Cox, D. R. (1972). Regression models and life-tables (with discussion). J. Roy. Stat. Soc. Ser. B 34, 187-220.
5. Dewanji, A. (1992). A note on a test for competing risks with missing failure type. Biometrika 79, 855-857.
6. Goetghebeur, E. & Ryan, L. (1990). A modified log rank test for competing risks with missing failure type. Biometrika 77, 207-211.
7. Goetghebeur, E. & Ryan, L. (1995). Analysis of competing risks survival data when some failure types are missing. Biometrika 82, 821-833.
8. Kalbfleisch, J. D. & Prentice, R. L. (2002). The statistical analysis of failure time data. Wiley, New York.
9. Lawless, J. F. & Nadeau, C. (1995). Some simple robust methods for the analysis of recurrent events. Technometrics 37, 158-168.
10. Lin, D. Y., Wei, L. J., Yang, I. & Ying, Z. (2000). Semiparametric regression for the mean and rate functions of recurrent events. J. Roy. Slat. Soc. Sci: B 62, 711-730.
11. Little, R. J. A. & Rubin, D. B. (2002). Statistical analysis with missing data. Wiley, New York.
12. Lu, K. & Tsiatis, A. A. (2001). Multiple imputation methods for estimating regression coefficients in the competing risks model with missing cause of failure. Biometrics 57, 1191-1197.
13. Pepe, M. S. & Cai, J. (1993). Some graphical displays and marginal regression analyses for recurrent failure times and time-dependent covariates. J. Amer. Statist. Assoc. 88, 811-820.
14. Pollard, D. (1990). Empirical processes: theory and applications. Institute of Mathematical Statistics, Hayward.
15. Prentice, R. L., Williams, B. J. & Peterson, A. V. (1981). On the regression analysis of multivariate failure time data. Biometrika 68, 373-389.
16. Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. Wiley, New York.
17. Schaubel, D. (2002). Semiparametric rate and hazard regression methods for sequentially ordered multivariate failure time data. Doctoral Dissertation, Department of Biostatistics University of North Carolina.
18. Tsiatis, A. A., Davidian, M. & McNeney, B. (2002). Multiple imputation methods for testing treatment differences in survival distributions with missing cause of failure. Biometrlka 89, 238-244.
19. van der Vaart, A. W. (2000). Asymptotic statistics. Cambridge University Press, Melbourne.
20. van der Vaart, A. W. & Wellner, J. A. (1996). Weak con vergence and empirical processes. Springer, New York.
21. Wei, L. J., Lin, D. Y. & Weissfeld, L. (1989). Regression analysis of multivariate incomplete failure time data by modelling marginal distributions. J. Amer. Statist. Assoc. 84, 1065-1073.