title: Marginal models for clustered time-to-event data with competing risks using pseudovalues.
abstract: 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.
authors.affiliation: Division of Biostatistics, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, Wisconsin 53226, USA. blogan@mcw.edu
authors.forename: Brent R
authors.initials: BR
authors.lastname: Logan
authors.forename: Mei-Jie
authors.initials: MJ
authors.lastname: Zhang
authors.forename: John P
authors.initials: JP
authors.lastname: Klein
mesh_terms: D000293:Adolescent; D000328:Adult; D001699:Biometry / Q000379:methods*; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D018380:Hematopoietic Stem Cell Transplantation / Q000401:mortality*; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000401:mortality* / Q000601:surgery*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D014481:United States / Q000453:epidemiology; D055815:Young Adult