A regression model for the conditional probability of a competing event: Application to monoclonal gammopathy of unknown significance

Journal of the Royal Statistical Society. Series C: Applied Statistics

Volumn 60, Issue 1, 2011, Pages 135-142

  Allignol, Arthur ^a   Latouche, Aurélien ^b   Yan, Jun ^c   Fine, Jason P ^d  

^a UNIVERSITY OF FREIBURG   (Germany)

^b LABORATOIRE DES SCIENCES DU CLIMAT ET DE L ENVIRONNEMENT   (France)

^c UNIVERSITY OF CONNECTICUT   (United States)

^d UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

Cause specific hazard; Competing risks; Conditional cumulative incidence curve; Proportional odds model

Indexed keywords

EID: 79952599212     PISSN: 00359254     EISSN: 14679876     Source Type: Journal    
DOI: 10.1111/j.1467-9876.2010.00729.x     Document Type: Article

References (16)

1. Cox, D. R. (1972) Regression models and life-tables (with discussion). J. R. Statist. Soc. B, 34, 187-220.
2. Fine, J. and Gray, R. (1999) A proportional hazard model for the subdistribution of a competing risk. J. Am. Statist. Ass., 94, 496-509.
3. Fine, J., Yan, J. and Kosorok, M. (2004) Temporal process regression. Biometrika, 91, 683-703.
4. Gray, R. (2006) cmprsk: subdistribution analysis of competing risks. R Package Version 2.1-7.
5. Kyle, R. (1993) Benign monoclonal gammopathy-after 20 to 35 years of follow-up. Mayo Clin. Proc., 64, 26-36.
6. Kyle, R. A. and Rajkumar, S. V. (2003) Monoclonal gammopathies of undetermined significance: a review. Immunol. Rev., 194, 112-139.
7. Lumley, T. (2004) The survival package. R News, 4, 26-28.
8. Lunn, M. (1998) Applying k-sample tests to conditional probabilities for competing risks in a clinical trial. Biometrics, 54, 1662-1672.
9. Martinussen, T. and Scheike, T. H. (2006) Dynamic Regression Models for Survival Data. Berlin: Springer.
10. Pepe, M. (1991) Inference for events with dependent risks in multiple endpoint studies. J. Am. Statist. Ass., 86, 770-778.
11. Pepe, M. and Mori, M. (1993) Kaplan-Meier, marginal or conditional probability curves in summarizing competing risks failure time data? Statist. Med., 12, 737-751.
12. Prentice, R., Kalbfleisch, J., Peterson, A. J., Flournoy, N., Farewell, V. and Breslow, N. E. (1978) The analysis of failure times in the presence of competing risks. Biometrics, 34, 541-554.
13. Scheike, T., Martinussen, T. and Silver, J. (2008) timereg: timereg package. R Package Version 1.1-0.
14. Scheike, T. and Zhang, M. (2008) Flexible competing risks regression modeling and goodness-of-fit. Liftime Data Anal., 14, 464-483.
15. Therneau, T. and Grambsch, P. (2001) Modeling survival data: extending the Cox model. In Statistics for Biology and Health. New York: Springer.
16. Yan, J. (2007) tpr: Temporal Process Regression. R Package Version 0.2-3.