The graft versus leukemia effect after bone marrow transplantation: A case study using structural nested failure time models

Biometrics

Volumn 55, Issue 1, 1999, Pages 23-28

  Keiding, Niels ^a   Filiberti, Marusca ^a   Esbjerg, Sille ^a   Robins, James M ^a   Jacobsen, Niels ^a  

^a NONE

Author keywords

Accelerated failure time; Causal inference; Competing risks; Counterfactuals; Cox regression model; Survival synthesis; Time dependent confounder

Indexed keywords

BONE; DISEASES; GRAFTS; REGRESSION ANALYSIS;

ACCELERATED FAILURE TIME; CAUSAL INFERENCES; COMPETING RISKS; CONFOUNDER; COUNTERFACTUALS; COX'S REGRESSION MODEL; FAILURE TIME; SURVIVAL SYNTHESIS; TIME DEPENDENT; TIME-DEPENDENT CONFOUNDER;

GRAFTING (CHEMICAL);

BONE MARROW TRANSPLANTATION; CANCER EPIDEMIOLOGY; CANCER RECURRENCE; CANCER RISK; CONTROLLED STUDY; COVARIANCE; DISEASE ASSOCIATION; GRAFT VERSUS HOST REACTION; HUMAN; LEUKEMIA; PREDICTION; PROGNOSIS; REGRESSION ANALYSIS; RELIABILITY; REVIEW; STATISTICAL MODEL; VALIDATION PROCESS;

TURDUS;

EID: 0032930005     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.0006-341X.1999.00023.x     Document Type: Review

References (21)

1. Andersen, P. K., Borgan, Ø., Gill, R. D., and Keiding, N. (1993). Statistical Models Based on Counting Processes. New York: Springer.
2. Arjas, E. and Eerola, M. (1993). On predictive causality in longitudinal studies. Journal of Statistical Planning and Inference 34, 361-384.
3. Cox, D. R. and Oakes, D. (1984). Analysis of Survival Data. London: Chapman and Hall.
4. Dabrowska, D. M., Sun, G.-W., and Horowitz, M. M. (1994). Cox regression in a Markov renewal model: An application to the analysis of bone marrow transplant data. Journal of the American Statistical Association 89, 867-877.
5. Eerola, M. (1994). Probabilistic Causality in Longitudinal Studies, Volume 92, Lecture Notes in Statistics. Berlin: Springer.
6. Fisher, L. and Kanarek, P. (1974). Presenting censored survival data when censoring and survival times may not be independent. In Reliability and Biometry: Statistical Analysis of Lifelength, F. Proschan and R. J. Serfling (eds), 303-326. Philadelphia: SIAM.
7. Jacobsen, N., Badsberg, J. H., Lönnqvist, B., et al., for the Nordic Bone Marrow Transplantation Group. (1987). Predictive factors for chronic graft-versus-host disease and leukaemic relapse after allogeneic bone marrow transplantation. In Recent Advances and feature Directions in Bone Marrow Transplantation, S. J. Baum, G. W. Santon, and F. Takaku (eds), 161-164. New York: Springer.
8. Jacobsen, N., Badsberg, J. H., Lönnqvist, B., Ringdén, O., Volin, L., Rajantie, J., Nikoskelainen, J., and Keiding, N., for the Nordic Bone Marrow Transplantation Group. (1990). Graft-versus-leukaemia activity associated with CMV-seropositive donor, post-transplant CMV infection, young donor age and chronic graft-versus-host disease in bone marrow allograft recipients. Bone Marrow Transplantation 5, 413-418.
9. Kalbfleisch, J. D. and Prentice, R. L. (1980). The Statistical Analysis of Failure Time Data. New York: Wiley.
10. Klein, J. P., Keiding, N., and Copelan, E. A. (1993). Plotting summary predictions in multistate survival models: Probabilities of relapse and death in remission for bone marrow transplantation patients. Statistics in Medicine 12, 2315-2332.
11. Lin, D. Y. and Wei, L. J. (1989). The robust inference for the Cox proportional hazards model. Journal of the American Statistical Association 84, 1074-1078.
12. Pepe, M. S. and Mori, M. (1993). Kaplan-Meier, marginal or conditional probability curves in summarizing competing risks failure time data? Statistics in Medicine 12, 737-751.
13. Prentice, R. L., Kalbfleisch, J. D., Peterson, Jr., A. V., Flournoy, N., Farewell, V. T., and Breslow, N. E. (1978). The analysis of failure times in the presence of competing risks. Biometrics 34, 541-554.
14. Robins, J. M. (1986). A new approach to causal inference in mortality studies with sustained exposure periods -Application to control of the healthy worker survivor effect. Mathematical Modelling 7, 1393-1512.
15. Robins, J. M. (1987). Addendum to "A new approach to causal inference in mortality studies with sustained exposure periods - Application to control of the healthy worker survivor effect." Computers and Mathematics with Applications 14, 923-945.
16. Robins, J. M. (1989). The analysis of randomized and nonrandomized AIDS treatment trials using a new approach to causal inference in longitudinal studies. In Health Service Research Methodology: A Focus on AIDS, L. Sechrest, H. Freeman, and A. Mulley A. (eds), 113-159. Washington, D.C.: NCHSR, U.S. Public Health Service.
17. Robins, J. M. (1992). Estimation of the time-dependent accelerated failure time model in the presence of confounding factors. Biometrika 79, 321-334.
18. Robins, J. M. (1993). Analytic methods for estimating HIV-treatment and cofactor effects. In Methodological Issues in AIDS Research, D. G. Ostrow and R. C. Kessler (eds), 213-290. New York: Plenum.
19. Robins, J. M. (1998). Structural nested failure time models. In Encyclopedia of Biostatistics, Volume 6, P. Armitage and T. E. Colton (eds), 4372-4389. Chichester: Wiley.
20. Robins, J. M. and Rotnitzky, A. (1992). Recovery of information and adjustment for dependent censoring using surrogate markers. In AIDS Epidemiology, Methodological Issues, N. P. Jewell, K. Dietz, and V. T. Farewell (eds), 297-331. Boston: Birkhäuser.
21. Robins, J. M., Blevins, D., Ritter, G., and Wulfsohn, M. (1992). G-estimation of the effect of prophylaxis therapy for Pneumocystis carinii pneumonia on the survival of AIDS patients. Epidemiology 3, 319-336.