Flexible competing risks regression modeling and goodness-of-fit. Lifetime Data Anal 2008 Dec;14(4):464-83
Date
08/30/2008Pubmed ID
18752067Pubmed Central ID
PMC2715961DOI
10.1007/s10985-008-9094-0Scopus ID
2-s2.0-55949105766 (requires institutional sign-in at Scopus site) 78 CitationsAbstract
In this paper we consider different approaches for estimation and assessment of covariate effects for the cumulative incidence curve in the competing risks model. The classic approach is to model all cause-specific hazards and then estimate the cumulative incidence curve based on these cause-specific hazards. Another recent approach is to directly model the cumulative incidence by a proportional model (Fine and Gray, J Am Stat Assoc 94:496-509, 1999), and then obtain direct estimates of how covariates influences the cumulative incidence curve. We consider a simple and flexible class of regression models that is easy to fit and contains the Fine-Gray model as a special case. One advantage of this approach is that our regression modeling allows for non-proportional hazards. This leads to a new simple goodness-of-fit procedure for the proportional subdistribution hazards assumption that is very easy to use. The test is constructive in the sense that it shows exactly where non-proportionality is present. We illustrate our methods to a bone marrow transplant data from the Center for International Blood and Marrow Transplant Research (CIBMTR). Through this data example we demonstrate the use of the flexible regression models to analyze competing risks data when non-proportionality is present in the data.
Author List
Scheike TH, Zhang MJAuthor
Mei-Jie Zhang PhD Professor in the Institute for Health and Equity department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
Bone Marrow TransplantationData Interpretation, Statistical
Humans
Incidence
Proportional Hazards Models
Recurrence
Regression Analysis
Risk Factors
Statistics, Nonparametric
Survival Analysis
