Inference for the effect of treatment on survival probability in randomized trials with noncompliance and administrative censoring

Biometrics

Volumn 67, Issue 4, 2011, Pages 1397-1405

  Nie, Hui ^a   Cheng, Jing ^b   Small, Dylan S ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Administrative censoring; Empirical likelihood; Instrumental variable; Noncompliance; Survival probability; Weibull parametric model

Indexed keywords

WEIBULL DISTRIBUTION;

ADMINISTRATIVE CENSORING; EMPIRICAL LIKELIHOOD; FOLLOW UP; INSTRUMENTAL VARIABLES; NONCOMPLIANCE; PARAMETRIC MODELS; PLUG-INS; SURVIVAL PROBABILITIES; WEIBULL; WEIBULL PARAMETRIC MODEL;

MAXIMUM LIKELIHOOD ESTIMATION;

ADMINISTRATION; COMPLIANCE; DATA SET; DISEASE TREATMENT; ESTIMATION METHOD; MORTALITY; NUMERICAL MODEL; PARAMETERIZATION; SURVIVAL; THEORETICAL STUDY;

EID: 83655183087     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2011.01575.x     Document Type: Article

References (26)

1. Angrist, J. D., Imbens, G. W., and Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association 91, 444-455.
2. Baker, S. G. (1998). Analysis of survival data from a randomized trial with all-or-none compliance: Estimating the cost-effectiveness of a cancer screening program. Journal of the American Statistical Association 93, 929-934.
3. Baker, S. G. and Lindeman, K. S. (1994). The paired availability design: A proposal for evaluating epidural analgesia during labor. Statistics in Medicine 13, 2269-2278.
4. Bound, J., Jaeger, D. A., and Baker, R. M. (1995). Problems with instrumental variables estimation when the correlation between the instruments and the endogeneous explanatory variable is weak. Journal of the American Statistical Association 90, 443-450.
5. Cheng, J. and Small, D. S. (2006). Bounds on causal effects in three-arm trials with noncompliance. Journal of the Royal Statistical Society, Series B (Methodological) 68, 815-836.
6. Cheng, J., Qin, J., and Zhang, B. (2009). Semiparametric estimation and inference for distributional and general treatment effects. Journal of the Royal Statistical Society, Series B (Methodological) 71, 881-904.
7. Cheng, J., Small, D. S., Tan, Z., and Ten Have, T. R. (2009). Efficient nonparametric estimation of causal effects in randomized trials with noncompliance. Biometrika 96, 19-36.
8. Cuzick, J., Sasieni, P., Myles, J., and Tyler, J. (2007). Estimating the effect of treatment in a proportional hazards model in the presence of non-compliance and contamination. Journal of the Royal Statistical Society, Series B (Methodological) 69, 565-588.
9. Efron, B. and Tibshirani, R. J. (1994). An Introduction to the Bootstrap. Boca Raton, Florida: Chapman & Hall/CRC.
10. Follmann, D. A. (2000). On the effect of treatment among would-be treatment compliers: An analysis of the multiple risk factor intervention trial. Journal of the American Statistical Association 95, 1101-1109.
11. Imbens, G. W. and Angrist, J. D. (1994). Identification and estimation of local average treatment effects. Econometrica 62, 467-476.
12. Imbens, G. W. and Rubin, D. B. (1997). Bayesian inference for causal effects in randomized experiments with noncompliance. The Annals of Statistics 25, 305-327.
13. Joffe, M. M. (2001). Administrative and artificial censoring in censored regression models. Statistics in Medicine 20, 2287-2304.
14. Kaplan, E. L. and Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53, 457-481.
15. Kubik, A., Parkin, D. M., Khlat, M., Erban, J., Polak, J., and Adamec, M. (1990). Lack of benefit from semi-annual screening for cancer of the lung: Follow-up report of a randomized controlled trial on a population of high-risk males in Czechoslovakia. International Journal of Cancer 45, 26-33.
16. Loeys, T. and Goetghebeur, E. (2003). A causal proportional hazards estimator for the effect of treatment actually received in a randomized trial with all-or-nothing compliance. Biometrics 59, 100-105.
17. Nagar, A. L. (1959). The bias and moment matrix of the general k-class estimators of the parameters in simultaneous equations. Econometrica 27, 575-595.
18. Oken, M. M., Marcus, P. M., Hu, P., Beck, T. M., Hocking, W., Kvale, P. A., Cordes, J., Riley, R. L., Winslow, S. D., Peace, S., Levin, D. L., Prorok, P. C., Gohagan, J. K., and for the PLCO Project Team. (2005). Baseline chest radiograph for lung cancer detection in the randomized prostate, lung, colorectal and ovarian cancer screening trial. Journal of the National Cancer Institute 97, 1832-1839.
19. Owen, A. B. (2001). Empirical Likelihood. Boca Raton, Florida: Chapman & Hall/CRC.
20. Robins, J. M. and Tsiatis, A. A. (1991). Correcting for non-compliance in randomized trials using rank preserving structural failure time models. Communications in Statistics, Theory and Methods 20, 2609-2631.
21. Rubin, D. B. (1978). Bayesian inference for causal effects. The Annals of Statistics 6, 34-58.
22. Sheiner, L. B. and Rubin, D. B. (1995). Intention-to-treat analysis and the goals of clinical trials. Clinical Pharmacology and Therapeutics 57, 6-15.
23. Small, D. S., Ten Have, T. R., Joffe, M. M., and Cheng, J. (2006). Random effects logistic models for analyzing efficacy of a longitudinal randomized treatment with non-adherence. Statistics in Medicine 25, 1981-2007.
24. Sommer, A. and Zeger, S. L. (1991). On estimating efficacy from clinical trials. Statistics in Medicine 10, 45-52.
25. Stock, J. H., Wright, J. H., and Yogo, M. (2002). A survey of weak instruments and weak identification in generalized method of moments. Journal of Business and Economic Statistics 20, 518-529.
26. Zelen, M. (1979). A new design for randomized clinical trials. New England Journal of Medicine 300, 1242-1245.