A dependent Dirichlet process model for survival data with competing risks

Yushu Shi (), Purushottam Laud () and Joan Neuner ()
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Yushu Shi: University of Missouri, Columbia
Purushottam Laud: Medical College of Wisconsin, CAPS
Joan Neuner: Medical College of Wisconsin, CAPS

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2021, vol. 27, issue 1, No 7, 156-176

Abstract: 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.

Keywords: Survival analysis; Competing risks; Nonparametric Bayesian model; Time-dependent covariate (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10985-020-09506-0

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