Nonparametric Transformation Models for Double-Censored Data with Crossed Survival Curves: A Bayesian Approach

Ping Xu, Ruichen Ni, Shouzheng Chen, Zhihua Ma () and Chong Zhong ()
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Ping Xu: School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
Ruichen Ni: School of Astronautics, Harbin Institute of Technology, Harbin 150006, China
Shouzheng Chen: Department of Applied Social Sciences, The Hong Kong Polytechnic University, Hong Kong 999077, China
Zhihua Ma: College of Economics, Shenzhen University, Shenzhen 518060, China
Chong Zhong: Department of Data Science and AI, The Hong Kong Polytechnic University, Hong Kong 999077, China

Mathematics, 2025, vol. 13, issue 15, 1-18

Abstract: Double-censored data are frequently encountered in pharmacological and epidemiological studies, where the failure time can only be observed within a certain range and is otherwise either left- or right-censored. In this paper, we present a Bayesian approach for analyzing double-censored survival data with crossed survival curves. We introduce a novel pseudo-quantile I-splines prior to model monotone transformations under both random and fixed censoring schemes. Additionally, we incorporate categorical heteroscedasticity using the dependent Dirichlet process (DDP), enabling the estimation of crossed survival curves. Comprehensive simulations further validate the robustness and accuracy of the method, particularly under the fixed censoring scheme, where traditional approaches may NOT be applicable. In the randomized AIDS clinical trial, by incorporating the categorical heteroscedasticity, we obtain a new finding that the effect of baseline log RNA levels is significant. The proposed framework provides a flexible and reliable tool for survival analysis, offering an alternative to parametric and semiparametric models.

Keywords: bayesian analysis; double censoring; heteroscedasticity; transformation models (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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