Marginal Models for Clustered Time-to-Event Data with Competing Risks Using Pseudovalues

Biometrics

Volumn 67, Issue 1, 2011, Pages 1-7

  Logan, Brent R ^a   Zhang, Mei Jie ^a   Klein, John P ^a  

^a MEDICAL COLLEGE OF WISCONSIN   (United States)

Author keywords

Clustered data; Cumulative incidence; Generalized estimating equations; Marginal model; Pseudoobservations; Sandwich variance

Indexed keywords

CLUSTERED DATUM; COMPETING RISKS; CUMULATIVE INCIDENCE; GENERALIZED ESTIMATING EQUATIONS; INCIDENCE FUNCTION; MARGINAL MODELS; PSEUDO-OBSERVATIONS; SANDWICH VARIANCE; TIME TO EVENTS; WITHIN CLUSTERS;

HAZARDS;

CLUSTER ANALYSIS; DATA SET; NUMERICAL MODEL; SOFTWARE;

EID: 79952586078     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2010.01416.x     Document Type: Article

References (15)

1. Andersen, P. K., Klein, J. P., and Rosthoj, S. (2003). Generalized linear models for correlated pseudo-observations with applications to multi-state models. Biometrika 90, 15-27.
2. Chen, B. E., Kramer, J. L., Greene, M. H., and Rosenberg, P. S. (2007). Competing risks analysis of correlated failure time data. Biometrics 64, 172-179.
3. Commenges, D. and Andersen, P. K. (1995). Score test of homogeneity for survival data. Lifetime Data Analysis 1, 145-156.
4. Fine, J. P. and Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association 94, 496-509.
5. Graw, F., Gerds, T. A., and Schumacher, M. (2009). On pseudo-values for regression analysis in competing risks models. Lifetime Data Analysis 15, 241-255.
6. Hougaard, P. (2000). Analysis of Multivariate Survival Data. New York: Springer-Verlag.
7. Katsahian, S., Resche-Rigon, M., Chevret, S., and Porcher, R. (2006). Analyzing multicentre competing risks data with a mixed proportional hazards model for the subdistribution. Statistics in Medicine 25, 4267-4278.
8. Klein, J. P. and Andersen, P. K. (2005). Regression modeling of competing risks data based on pseudo-values of the cumulative incidence function. Biometrics 61, 223-229.
9. Klein, J. P., Logan, B., Harhoff, M., and Andersen, P. K. (2007). Analyzing survival curves at a fixed point in time. Statistics in Medicine 26, 4505-4519.
10. Klein, J. P., Gerster, M., Andersen, P. K., Tarima, S., and Perme, M. P. (2008). SAS and R functions to compute pseudo-values for censored data regression. Computer Methods and Programs in Biomedicine 89, 289-300.
11. Lee, E. W., Wei, L. J., and Amato, D. A. (1992). Cox-type regression analysis for large numbers of small groups of correlated failure time observations. In Survival Analysis: State of the Art, J. P. Klein and P. K. Goel (eds), 237-247. Dordrecht: Kluwer Academic.
12. Liang, K.-Y. and Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika 78, 13-22.
13. Satten, G. A. and Datta, S. (2001). Inverse-probability-of-censoring weighted average. American Statistician 55, 207-210.
14. Scheike, T. H., Zhang, M.-J., and Gerds, T. A. (2008). Predicting cumulative incidence probability by direct binomial regression. Biometrika 95, 205-220.
15. Spiekerman, C. F. and Lin, D. Y. (1998). Marginal regression models for multivariate failure time data. Journal of the American Statistical Association 93, 1164-1175.