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A dependent Dirichlet process model for survival data with competing risks

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Abstract

In this paper, we first propose a dependent Dirichlet process (DDP) model using a mixture of Weibull models with each mixture component resembling a Cox model for survival data. We then build a Dirichlet process mixture model for competing risks data without regression covariates. Next we extend this model to a DDP model for competing risks regression data by using a multiplicative covariate effect on subdistribution hazards in the mixture components. Though built on proportional hazards (or subdistribution hazards) models, the proposed nonparametric Bayesian regression models do not require the assumption of constant hazard (or subdistribution hazard) ratio. An external time-dependent covariate is also considered in the survival model. After describing the model, we discuss how both cause-specific and subdistribution hazard ratios can be estimated from the same nonparametric Bayesian model for competing risks regression. For use with the regression models proposed, we introduce an omnibus prior that is suitable when little external information is available about covariate effects. Finally we compare the models’ performance with existing methods through simulations. We also illustrate the proposed competing risks regression model with data from a breast cancer study. An R package “DPWeibull” implementing all of the proposed methods is available at CRAN.

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Acknowledgements

The study was funded by the American Cancer Society (RSG-11-098-01-CPHPS to JMN).

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Authors and Affiliations

  1. University of Missouri, Columbia, Middlebush Hall, Columbia, MO, 65201, USA

    Yushu Shi

  2. Medical College of Wisconsin, CAPS, 8701 Watertown Plank Rd, Milwaukee, WI, 53226, USA

    Purushottam Laud & Joan Neuner

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  1. Yushu Shi
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  2. Purushottam Laud
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  3. Joan Neuner
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Correspondence to Yushu Shi.

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Shi, Y., Laud, P. & Neuner, J. A dependent Dirichlet process model for survival data with competing risks. Lifetime Data Anal 27, 156–176 (2021). https://doi.org/10.1007/s10985-020-09506-0

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  • DOI: https://doi.org/10.1007/s10985-020-09506-0

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