One- and two-sample nonparametric inference procedures in the presence of a mixture of independent and dependent censoring

Biostatistics

Volumn 7, Issue 2, 2006, Pages 252-267

  Park, Yuhyun ^a   Tian, Lu ^b   Wei, L J ^a  

^a HARVARD UNIVERSITY   (United States)

^b NORTHWESTERN UNIVERSITY   (United States)

Author keywords

Competing risks; Martingale; Sensitivity analysis; Simultaneous confidence interval; Survival analysis

Indexed keywords

ANTI HUMAN IMMUNODEFICIENCY VIRUS AGENT;

ARTICLE; BIOMETRY; CLINICAL TRIAL; CONFIDENCE INTERVAL; CONTROLLED CLINICAL TRIAL; HEMOPHILIA A; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; METHODOLOGY; MORTALITY; NONPARAMETRIC TEST; RANDOMIZED CONTROLLED TRIAL; STATISTICS; SURVIVAL;

ANTI-HIV AGENTS; BIOMETRY; CONFIDENCE INTERVALS; HEMOPHILIA A; HIV INFECTIONS; HUMANS; RANDOMIZED CONTROLLED TRIALS; STATISTICS, NONPARAMETRIC; SURVIVAL ANALYSIS;

HUMAN IMMUNODEFICIENCY VIRUS;

EID: 33645568969     PISSN: 14654644     EISSN: 14684357     Source Type: Journal    
DOI: 10.1093/biostatistics/kxj005     Document Type: Article

References (37)

1. AALEN, O. (1978). Nonparametric estimation of partial transition probabilities in multiple decrement models. Annals of Statistics 6, 534-545.
2. BACCHETTI, P. (1990). Estimating the incubation period of AIDS by comparing population infection and diagnosis patterns. Journal of the American Statistical Association 85, 1002-1008.
3. BETENSKY, R. A., LINDSEY, J. C., RYAN, L. M. AND WAND, M. P. (2002). A local likelihood proportional hazards model for interval censored data. Statistics in Medicine 21, 263-275.
4. BETENSKY, R. A., RABINOWITZ, D. AND TSIATIS, A. A. (2001). Computationally simple accelerated failure time regression for interval censored data. Biometrika 88, 703-711.
5. CAI, T. AND BETENSKY, R. A. (2003). Hazard regression for interval censored data with penalized spline. Biometrics 59, 570-579.
6. DIRIENZO, A. G. (2003). Nonparametric comparison of two survival-time distributions in the presence of dependent censoring. Biometrics 59, 497-504.
7. DIRIENZO, A. G. AND LAGAKOS, S. W. (2001). Bias correction for score tests arising from misspecified proportional hazards regression models. Biometrika 88, 421-434.
8. FISHER, L. AND KANAREK, P. (1974). Presenting censored survival data when censoring and survival times may not be independent. In Proschan, F. and Serfling, R. (eds), Reliability and Biometry: Statistical Analysis of Lifelength. Philadelphia, PA: SIAM, pp. 303-326.
9. FLEMING, T. R. AND HARRINGTON, D. P. (1991). Counting Processes and Survival Analysis. New York: Wiley.
10. GENTLEMAN, R. AND GEYER, C. J. (1994). Maximum likelihood for interval censored data: consistency and computation. Biometrika 81, 618-623.
11. GOLDWASSER, M. A., TIAN, L. AND WEI, L. J. (2004). Statistical inference for infinite dimensional parameters via asymptotically pivotal estimating functions. Biometrika 91, 81-94.
12. HAMMER, S. M., KATZENSTEIN, D. A., HUGHES, M. D., GUNDACKER, H., SCHOOLEY., R. T., HAUBRICH, R. H., HENRY., W. K., LEDERMAN, M. M.,PHAIR, J. P., NIU, M. et al. (1996). A trial comparing nucleoside monotherapy with combination therapy in HIV-infected adults with CD4 cell counts from 200 to 500 per cubic millimeter. New England Journal of Medicine 335, 1081-1090.
13. HUANG, J. (1996). Efficient estimation for the proportional hazards model with interval censoring. Annals of Statistics 24, 540-568.
14. HUANG, J. (1999). Asymptotic properties of nonparametric estimation based on partly interval-censored data. Statistica Sinica 9, 501-519.
15. HUANG, J. AND WELLNER, J. A. (1997). Interval censored survival data: a review of recent progress. Proceedings of the First Seattle Symposium in Biostatistics: Survival Analysis. New York: Springer.
16. JOLY., P., COMMENCES, D. AND LETENNEUR, L. (1998). A penalized likelihood approach for arbitrarily censored and truncated data: application to age-specific incidence of dementia. Biometrics 54, 185-194.
17. KALBFLEISCH, J. D. AND PRENTICE, R. L. (2002). The Statistical Analysis of Failure Time Data, 2nd edition. New York: Wiley.
18. KLEIN, J. P. AND MOESCHBERGER, M. L. (1988). Bounds on net survival probabilities for dependent competing risks. Biometrics 44, 529-538.
19. KLEIN, J. P., MOESCHBERGER, M. L., Li, Y. H. AND WANG, S. T. (1992). Estimating random effects in the Framingham heart study (with discussion). In Klein, J. and Goel, P. (eds), Survival Analysis: State of the Art. Dordrecht, The Netherlands: Kluwer, pp. 99-120.
20. KOOPERBERG, C. AND CLARKSON, D. B. (1997). Hazard regression with interval-censored data. Biometrics 53, 1485-1494.
21. KRONER, B. L., ROSENBERG, P. S., ALEDORT, L. M., ALVORD, W. G. AND GOEDERT, J. J. (1994). HIV-1 infection incidence among persons with hemophilia in the United States and Western Europe, 1978-1990. Journal of Acquired Immune Deficiency Syndromes 7, 279-286.
22. LIN, D. Y., ROBINS, J. M. AND WEI, L. J. (1996). Comparing two failure time distributions in the presence of dependent competing risks. Biometrika 83, 381-393.
23. LIN, D. Y., WEI, L. J. AND YING, Z. (1993). Checking the Cox model with cumulative sums of martingale-based residuals. Biometrika 80, 557-572.
24. MOESCHBERGER, M. L. AND KLEIN, J. P. (1995). Statistical methods for dependent competing risks. Lifetime Data Analysis 1, 195-204.
25. PETERSON, A. V. (1976). Bounds for a joint distribution function with fixed sub-distribution functions: application to competing risks. Proceedings of the National Academy of Sciences of the United States of America 73, 11-13.
26. PETO, R. (1973). Experimental survival curves for interval-censored data. Applied Statistics 22, 86-91.
27. RABINOWITZ, D., TSIATIS, A. A. AND ARAGON, J. (1995). Regression with interval-censored data. Biometrika 82, 501-513.
28. ROBINS, J. M. (1993). Information recovery and bias adjustment in proportional hazards regression analysis of randomized trials using surrogate markers. In Proceedings of the Biopharmaceutical Section, American Statistical Association, pp. 24-33.
29. ROBINS, J. M. AND FINKELSTEIN, D. H. (2000). Correcting for non-compliance and dependent censoring in an AIDS clinical trial with inverse probability of censoring weighted (IPCW) log-rank test. Biometrics 56, 779-788.
30. ROBINS, J. M. AND ROTNITZKY, A. (1992). Recovery of information and adjustment for dependent censoring using surrogate markers. In Jewell, N. and Dietz, K. (eds), AIDS Epidemiology: Methodological Issues. Boston, MA: Birkhauser, pp. 297-331.
31. ROSENBERG, P. S. (1995). Hazard function estimation using B-splines. Biometrics 51, 874-887.
32. SATTEN, G. A., DATTA, S. AND ROBINS, J. M. (2001). An estimator for the survival function when data are subject to dependent censoring. Statistics and Probability Letters 54, 397-403.
33. SCHARFSTEIN, D. O. AND ROBINS, J. M. (2002). Estimation of the failure time distribution in the presence of informative censoring. Biometrika 89, 617-634.
34. SLUD, E. V. AND RUBINSTEIN, L. V. (1983). Dependent competing risks and summary survival curves. Biometrika 70, 643-649.
35. TSIATIS, A. A. (1975). A nonidentifiability aspect of the problem of competing risk. Proceedings of the National Academy of Sciences of the United States of America 72, 20-22.
36. TURNBULL, B. W. (1976). The empirical distribution function with arbitrary grouped, censored and truncated data. Journal of the Royal Statistical Society Series B: Statistical Methodology 38, 290-295.
37. ZHENG, M. AND KLEIN, J. P. (1995). Estimates of marginal survival for dependent competing risks based on an assumed copula. Biometrika 82, 127-138.