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. 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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Free PMC article

Bayesian Approach for Flexible Modeling of Semicompeting Risks Data

Baoguang Han et al. Stat Med. .
Free PMC article

Abstract

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.

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

Figures

Figure 1
Figure 1
Illness-death model framework
Figure 2
Figure 2
The estimated baseline cumulative hazards for the NSABP B-14 dataset based on the random intercept Cox restricted (left) and the random intercept Cox general models (right).
Figure 3
Figure 3
Prediction of the distant recurrence survival probability for a patient who experienced the local failure. The prediction was based on the multivariate random effects Cox general model. The posterior mean is the solid line while the 2.5% and 97.5% quartiles are dashed lines.
Figure 4
Figure 4
Prediction of the distant recurrence survival probability for a patient who did not experience the local failure. The prediction was based on the multivariate random effects Cox general model. The posterior mean is the solid line while the 2.5% and 97.5% quartiles are the dashed lines.

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