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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Heterogeneous Functional Regression for Subgroup Analysis.

Yeqing Zhou¹, Fei Jiang²

¹School of Mathematical Sciences, School of Economics and Management, and Key Laboratory of Intelligent Computing and Applications, Tongji University, Shanghai, China.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America

|August 11, 2025

Summary

This summary is machine-generated.

This study introduces a novel method for modeling heterogeneous functional regression relationships, effectively identifying subgroups and estimating parameters simultaneously. The approach ensures statistical guarantees and demonstrates strong performance in simulations and a real-world Alzheimer's disease study.

Keywords:

Alzheimer’s disease Amendable penalty Convex clustering High-dimensional regressions Subgroup analysis

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Area of Science:

Statistics
Data Science
Biostatistics

Background:

Modern datasets exhibit increasing complexity and heterogeneity.
Classical regression models often fail to capture variations across data subgroups.
Identifying and modeling these heterogeneous relationships is crucial for accurate analysis.

Purpose of the Study:

To propose a new approach for modeling heterogeneous functional regression relationships.
To identify underlying subgroups within data where relationships differ.
To model the association between a response and predictor as a function varying across subgroups.

Main Methods:

A novel procedure for simultaneous parameter estimation and subgroup identification.
Utilizes fusion-type group-wise penalization for modeling heterogeneity.
Establishes non-asymptotic convergence, oracle property, and asymptotic normality of estimators.

Main Results:

The proposed method effectively models heterogeneous functional regression.
Simultaneous estimation and subgroup identification are achieved with statistical guarantees.
Demonstrated performance through intensive simulations and application to Alzheimer's disease data.

Conclusions:

The developed method offers a robust solution for analyzing heterogeneous functional data.
Provides reliable parameter estimation and subgroup identification.
Applicable to complex datasets in various scientific fields, including biomedical research.