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Related Experiment Video

Updated: Jan 19, 2026

Creation of a Knee Joint-on-a-Chip for Modeling Joint Diseases and Testing Drugs

Creation of a Knee Joint-on-a-Chip for Modeling Joint Diseases and Testing Drugs

Published on: January 27, 2023

Variable selection for joint models with time-varying coefficients.

Yujing Xie¹, Zangdong He^2,3, Wanzhu Tu⁴

¹School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China.

Statistical Methods in Medical Research

|September 13, 2019

Summary

This summary is machine-generated.

This study introduces advanced joint models with time-varying coefficients for analyzing longitudinal and survival data. It offers a data-driven approach for variable selection, improving clinical study analysis.

Keywords:

B-spline Gaussian quadrature adaptive LASSO

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Last Updated: Jan 19, 2026

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Published on: January 27, 2023

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Area of Science:

Biostatistics
Clinical Data Analysis
Longitudinal Data Modeling

Background:

Clinical studies often involve concurrent collection of longitudinal and survival data.
Standard joint models assume time-invariant coefficients, which can be overly restrictive in practice.

Purpose of the Study:

To extend standard joint models to incorporate time-varying coefficients for both longitudinal and survival components.
To develop a data-driven method for variable selection in these extended joint models.

Main Methods:

Utilized B-spline decomposition and penalized likelihood with adaptive group LASSO for variable selection.
Distinguished time-varying and time-invariant effects using the proposed methodology.
Employed Gaussian-Legendre and Gaussian-Hermite quadratures for integral approximation.

Main Results:

Simulation studies demonstrated good performance in both variable selection and parameter estimation.
The proposed method effectively handles complex relationships in longitudinal and survival data.

Conclusions:

The extended joint model with time-varying coefficients provides a more flexible and accurate approach for analyzing concurrent longitudinal and survival data.
The data-driven variable selection method enhances the interpretability and applicability of joint models in clinical research, as shown in the primary biliary cirrhosis study analysis.