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Stat Med. 2014 Dec 20;33(29):5111-25. doi: 10.1002/sim.6313. Epub 2014 Oct 2.

Bayesian approach for flexible modeling of semicompeting risks data.

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Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, 46285, U.S.A.


Semicompeting risks data arise when two types of events, non-terminal and terminal, are observed. When the terminal event occurs first, it censors the non-terminal event, but not vice versa. To account for possible dependent censoring of the non-terminal event by the terminal event and to improve prediction of the terminal event using the non-terminal event information, it is crucial to model their association properly. Motivated by a breast cancer clinical trial data analysis, we extend the well-known illness-death models to allow flexible random effects to capture heterogeneous association structures in the data. Our extension also represents a generalization of the popular shared frailty models that usually assume that the non-terminal event does not affect the hazards of the terminal event beyond a frailty term. We propose a unified Bayesian modeling approach that can utilize existing software packages for both model fitting and individual-specific event prediction. The approach is demonstrated via both simulation studies and a breast cancer data set analysis.


Markov chain Monte Carlo; illness-death; random effects; semicompeting risks

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