A classification tree approach for the modeling of competing risks in discrete time
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Cause-specific hazard models are a popular tool for the analysis of competing risks data. The classical modeling approach in discrete time consists of fitting parametric multinomial logit models. A drawback of this method is that the focus is on main effects only, and that higher order interactions are hard to handle. Moreover, the resulting models contain a large number of parameters, which may cause numerical problems when estimating coefficients. To overcome these problems, a tree-based model is proposed that extends the survival tree methodology developed previously for time-to-event models with one single type of event. The performance of the method, compared with several competitors, is investigated in simulations. The usefulness of the proposed approach is demonstrated by an analysis of age-related macular degeneration among elderly people that were monitored by annual study visits.
KeywordsDiscrete time-to-event data Competing risks Recursive partitioning Cause-specific hazards Regression modeling
Mathematics Subject Classification62N01 62N02 62P10 62-07
Support by the German Research Foundation (DFG), Grant SCHM 2966/1-2 and SCHM 2966/2-1, is gratefully acknowledged. The MODIAMD study is funded by the German Ministry of Education and Research (BMBF), Funding Number 13N10349.
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