Bayesian Semi-Parametric Regression for Quantile Residual Lifetime

Title & Authors
Bayesian Semi-Parametric Regression for Quantile Residual Lifetime
Park, Taeyoung; Bae, Wonho;

Abstract
The quantile residual life function has been effectively used to interpret results from the analysis of the proportional hazards model for censored survival data; however, the quantile residual life function is not always estimable with currently available semi-parametric regression methods in the presence of heavy censoring. A parametric regression approach may circumvent the difficulty of heavy censoring, but parametric assumptions on a baseline hazard function can cause a potential bias. This article proposes a Bayesian semi-parametric regression approach for inference on an unknown baseline hazard function while adjusting for available covariates. We consider a model-based approach but the proposed method does not suffer from strong parametric assumptions, enjoying a closed-form specification of the parametric regression approach without sacrificing the flexibility of the semi-parametric regression approach. The proposed method is applied to simulated data and heavily censored survival data to estimate various quantile residual lifetimes and adjust for important prognostic factors.
Keywords
Bayesian nonparametrics;heavy censoring;median residual lifetime function;partial collapse;survival analysis;
Language
English
Cited by
1.
비대칭적 점프확산 모형의 효율적인 베이지안 추론,박태영;이영은;

응용통계연구, 2014. vol.27. 6, pp.959-973
1.
Efficient Bayesian Inference on Asymmetric Jump-Diffusion Models, Korean Journal of Applied Statistics, 2014, 27, 6, 959
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