Semiparametric estimators for the regression coefficients in the linear transformation competing risks model with missing cause of failure

Biometrika

Volumn 92, Issue 4, 2005, Pages 875-891

  Gao, Guozhi ^a   Tsiatis, Anastasios A ^b  

^a AMGEN INC   (United States)

^b NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

Cause specific hazard; Competing risks; Doubly robust; Inverse probability weighted; Linear transformation model; Missing at random

Indexed keywords

EID: 27944480944     PISSN: 00063444     EISSN: 14643510     Source Type: Journal    
DOI: 10.1093/biomet/92.4.875     Document Type: Review

References (21)

1. ANDERSEN, J., GOETGHEBEUR, E. & RYAN, L. (1996). Missing cause of death information in the analysis of survival data. Statist. Mud. 15, 2191-201.
2. BENNETT, S. (1983). Analysis of survival data by the proportional odds model. Statist. Med. 2, 273-7.
3. CAI, T., WEI, L. J. & WILCOX, M. (2000). Semiparametric regression analysis for clustered failure time data. Biometrika 87, 867-78.
4. CHEN, K., JIN, Z. & YING, Z. (2002). Semiparametric analysis of transformation models with censored data. Biometrika 89, 659-68.
5. CHENG, S. C., WEI, L. J. & YINO, Z. (1995). Analysis of transformation models with censored data. Biometrika 82, 835-45.
6. CHENG, S. C., WEI, L. J. & YINO, Z. (1997). Prediction of survival probabilities with semi-parametric transformation models. J. Am. Statist. Assoc. 92, 227-35.
7. Cox, D. R. (1972). Regression models and life tables (with Discussion). J. R. Statist. Soc. B 34, 187-220.
8. Cox, D. R. (1975). Partial likelihood. Biometrika 62, 269-76.
9. FINE, J. P., YINO, Z. & WEI, L. J. (1998). On the linear transformation model with censored data. Biometrika 85, 980-6.
10. FLEMING, T. R. & HARRINGTON, D. P. (1991). Counting Process and Survival Analysis. New York: Wiley.
11. GOETGHEBEUR, E. & RYAN, L. (1995). Analysis of competing risks survival data when some failure types are missing. Biometrika 82, 821-34.
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, 1191-7.
13. LU, W. & YINO, Z. (2004). On semiparametric transformation cure models. Biometrika 91, 331-43.
14. MURPHY, S. A., ROSSINI, A. J. & VAN DER VAART, A. W. (1997). Maximum likelihood estimation in the proportional odds model. J. Am. Statist. Assoc. 92, 968-76.
15. PETTITT, A. N. (1982). Inference for the linear models using a likelihood based on ranks. J. R. Statist. Soc. B 44, 234-43.
16. PETTITT, A. N. (1984). Proportional odds model for survival data and estimates using ranks. Appl. Statist. 33, 169-75.
17. ROBINS, J. M., ROTNITZKY, A. & ZHAO, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. J. Am. Statist. Assoc. 89, 846-66.
18. RUBIN, D. B. (1976). Inference and missing data. Biometrika 63, 581-92.
19. RUBIN, D. B. (1987). Multiple Imputation for Nonreponse in Surveys. New York: Wiley.
20. RUBIN, D. B. (1996). Multiple imputation after 18+ years. J. Am. Statist. Assoc. 91, 473-89.
21. SCHARFSTEIN, D. O., ROTNITZKY, A. & ROBINS, J. M. (1994). Adjusting for nonignorable drop-out using semiparametric nonresponse models: rejoinder, J. Am. Statist. Assoc. 94, 1135-46.