Flexible competing risks regression modeling and goodness-of-fit |
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Authors: | Thomas H Scheike Mei-Jie Zhang |
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Institution: | (1) Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark;(2) Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WS, USA |
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Abstract: | 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. |
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Keywords: | Binomial modeling Competing risks Goodness-of-fit Inverse-censoring probability weighting Nonparametric effects Non-proportionality Regression effects |
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