Marginal Models for Clustered Time-to-Event Data with Competing Risks Using Pseudovalues
Article first published online: 6 APR 2010
© 2010, The International Biometric Society
Volume 67, Issue 1, pages 1–7, March 2011
How to Cite
Logan, B. R., Zhang, M.-J. and Klein, J. P. (2011), Marginal Models for Clustered Time-to-Event Data with Competing Risks Using Pseudovalues. Biometrics, 67: 1–7. doi: 10.1111/j.1541-0420.2010.01416.x
- Issue published online: 6 APR 2010
- Article first published online: 6 APR 2010
- Received June 2009. Revised February 2010. Accepted February 2010.
- Clustered data;
- Cumulative incidence;
- Generalized estimating equations;
- Marginal model;
- Sandwich variance
Summary Many time-to-event studies are complicated by the presence of competing risks and by nesting of individuals within a cluster, such as patients in the same center in a multicenter study. Several methods have been proposed for modeling the cumulative incidence function with independent observations. However, when subjects are clustered, one needs to account for the presence of a cluster effect either through frailty modeling of the hazard or subdistribution hazard, or by adjusting for the within-cluster correlation in a marginal model. We propose a method for modeling the marginal cumulative incidence function directly. We compute leave-one-out pseudo-observations from the cumulative incidence function at several time points. These are used in a generalized estimating equation to model the marginal cumulative incidence curve, and obtain consistent estimates of the model parameters. A sandwich variance estimator is derived to adjust for the within-cluster correlation. The method is easy to implement using standard software once the pseudovalues are obtained, and is a generalization of several existing models. Simulation studies show that the method works well to adjust the SE for the within-cluster correlation. We illustrate the method on a dataset looking at outcomes after bone marrow transplantation.