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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

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Multithreshold change plane model: Estimation theory and applications in subgroup identification.

Jialiang Li^1,2,3, Yaguang Li⁴, Baisuo Jin⁴

¹Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore.

Statistics in Medicine

|April 12, 2021

Summary

This summary is machine-generated.

This study introduces a new regression model to identify patient subgroups with distinct covariate effects. This method aids in personalized medicine by revealing underlying group structures and improving treatment strategies.

Keywords:

change plane induced smoothing penalty function precision medicine subgroup identification

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Area of Science:

Statistics
Biostatistics
Machine Learning

Background:

Identifying subgroups with varying covariate effects is crucial for personalized medicine.
Existing methods may not effectively handle complex subgroup structures or high-dimensional data.

Purpose of the Study:

To propose a novel multithreshold change plane regression model for subgroup identification.
To develop a robust two-stage estimation approach for determining subgroup number, thresholds, and parameters.
To enable sparse solutions in moderate- to high-dimensional covariate settings.

Main Methods:

A two-stage estimation approach is introduced.
Stage one utilizes a group selection principle for consistent subgroup number identification.
Stage two refines change point locations and model parameters using penalized induced smoothing.

Main Results:

The proposed model effectively partitions subjects into subgroups with different covariate effects.
The method allows for sparse solutions, suitable for moderate- to high-dimensional covariates.
Asymptotic properties of the estimators are established.

Conclusions:

The multithreshold change plane regression model offers a powerful tool for subgroup discovery.
The novel estimation approach provides consistent and refined parameter estimates.
This methodology has direct applications in personalized medicine for tailored treatment strategies.