Marginal models for clustered time-to-event data with competing risks using pseudovalues. Biometrics 2011 Mar;67(1):1-7
Date
04/10/2010Pubmed ID
20377579Pubmed Central ID
PMC2902638DOI
10.1111/j.1541-0420.2010.01416.xScopus ID
2-s2.0-79952586078 (requires institutional sign-in at Scopus site) 34 CitationsAbstract
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.
Author List
Logan BR, Zhang MJ, Klein JPAuthors
Brent R. Logan PhD Director, Professor in the Institute for Health and Equity department at Medical College of WisconsinMei-Jie Zhang PhD Professor in the Institute for Health and Equity department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
AdolescentAdult
Biometry
Cluster Analysis
Computer Simulation
Data Interpretation, Statistical
Female
Hematopoietic Stem Cell Transplantation
Humans
Leukemia, Myeloid, Acute
Male
Middle Aged
Models, Statistical
Risk Assessment
Risk Factors
United States
Young Adult
