Computationally Efficient Marginal Models for Clustered Recurrent Event Data

Biometrics

Volumn 68, Issue 2, 2012, Pages 637-647

  Liu, Dandan ^a   Schaubel, Douglas E ^b   Kalbfleisch, John D ^b  

^a VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b UNIVERSITY OF MICHIGAN SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Clustered recurrent event data; Interval grouped data; Large database; Marginal models; Piecewise constant; Proportional rates

Indexed keywords

CLUSTERED RECURRENT EVENT DATA; COHORT STUDIES; COMPUTATIONALLY EFFICIENT; INTERVAL-GROUPED DATA; LARGE DATABASE; MARGINAL MODELS; PIECE-WISE CONSTANTS; PROPORTIONAL RATE; RATE MODELS; RECURRENT EVENT DATUM;

DATABASE SYSTEMS;

BASELINE SURVEY; CLUSTER ANALYSIS; COHORT ANALYSIS; COMPUTER SIMULATION; DATABASE; HEALTH SERVICES; HOSPITAL SECTOR; NUMERICAL MODEL; SOFTWARE;

EID: 84862905482     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2011.01676.x     Document Type: Article

References (18)

1. Allison, P. D. (2010). Survival Analysis using SAS: A Practical Guide, 2nd edition. North Carolina: SAS Publishing.
2. Andersen, P. K. and Gill, R. D. (1982). Cox's regression model for counting process: A large sample study. Annals of Statistics 10, 1100-1120.
3. Cook, R. and Lawless, J. (1997). Marginal analysis of recurrent events and a terminal event. Statistics in Medicine 16, 911-924.
4. Cook, R. and Lawless, J. (2007). The Statistical Analysis of Recurrent Events. New York : Springer.
5. Ghosh, D. and Lin, D. (2002). Marginal regression models for recurrent and terminal events. Statistica Sinica 12, 663-688.
6. Jacobsen, M. (1989). Existence and unicity of mles in discrete exponential family distribution. Scandinavian Journal of Statistics 16, 335-349.
7. Kalbfleisch, J. D. and Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data, 2nd edition. New York: Wiley.
8. Lawless, J. and Nadeau, C. (1995). Some simple robust methods for the analysis of recurrent events. Technometrics 37, 158-168.
9. Lawless, J. and Zhan, M. (1998). Analysis of interval-grouped recurrent-event data using piecewise constant rate functions. The Canadian Journal of Statistics 26, 549-565.
10. Lin, D., Wei, L., Yang, I., and Ying, Z. (2000). Semiparametric regression for the mean and rate functions of recurrent events. Journal of the Royal Statistical Society, Series B 62, 771-730.
11. Rockafellar, R. T. (1970). Convex Analysis. Princeton, New Jersey : Princeton University Press.
12. Schaubel, D. and Cai, J. (2005). Semiparametric methods for clustered recurrent event data. Lifetime Data Analysis 11, 405-425.
13. Schaubel, D. and Zhang, M. (2010). Estimating treatment effects on the marginal recurrentevent mean in the presence of a terminating event. Lifetime Data Analysis 16, 451-477.
14. Sun, J. and Kalbfleisch, J. (1995). Estimation of the mean function of point processes based on panel count data. Statistica Sinica 5, 279-290.
15. Sun, J. and Wei, L. (2000). Regression analysis of panel count data with covariate-dependent observation and censoring times. Journal of the Royal Statistical Society, Series B 62, 293-302.
16. Thall, P. and Lachin, J. (1988). Analysis of recurrent events: Nonparametric methods for random-interval count data. Journal of the American Statistical Association 83, 339-347.
17. Wellner, J. and Zhang, Y. (2000). Two estimators of the mean of a counting process with panel count data. The Annals of statistics 28, 779-814.
18. Ye, Y., Kalbfleisch, J., and Schaubel, D. (2007). Semiparametric analysis of correlated recurrent and terminal events. Biometrics 63, 78-87.