國立臺北大學統計學系

專題演講

 

講題：Bayesian Joint Modeling with Heteroscedastic Covariance for Longitudinal and Time-to-Event Data

主講人：李國榮 教授 (國立成功大學 統計學系)

時間：115 年 04 月 22 日 (星期三，13：10~15：00)

地點：三峽校區商學院3F13教室

Abstract

In clinical and epidemiological research, understanding the interplay between longitudinal biomarker measurements and time-to-event outcomes is critical for disease modeling and risk prediction. This paper presents a new Bayesian joint modeling framework that integrates a linear mixed-effects model (LMM) for longitudinal data and a Cox proportional hazards model for survival outcomes. The framework introduces a flexible covariance structure for longitudinal outcomes using hypersphere decomposition within the variance-correlation decomposition (HDVCD) framework. This method ensures positive definiteness while capturing serial correlations and subject-level heterogeneity. We explore three distinct association structures, random-effects model (REM), shared parameter model (SPM), and trajectory model (TM) to capture the relationship between the longitudinal and survival processes. Simulation studies and real-world applications demonstrate the robustness and enhanced predictive performance of the proposed models, particularly in handling complex covariance structures. This framework advances the flexibility and reliability of traditional joint modeling approaches.

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國立臺北大學統計學系  敬邀

115.04.09

附件：演講摘要