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Abstract The presence of censored data can induce nonlinear covariate effects, unobserved random variation in individual outcomes, and complex patterns in the underlying hazard function that can be difficult to incorporate into conventional parametric Cox models. Using a Gaussian process (GP) approach within a Bayesian context, we extend the conventional Cox model to incorporate nonlinear covariate effects and random effects for subject-level residuals.
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Advanced search Journal of Applied Statistics Latest Articles Submit an article Journal homepage Full Article Figures & data References Citations Metrics Reprints & Permissions Read this article /doi/full/10.1080/02664763.2024.2338404?needAccess=true ABSTRACT In survival analysis, the Accelerated Failure Time (AFT) shared frailty model is a widely used framework for analyzing time-to-event data while accounting for unobserved heterogeneity among individuals.
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