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Subgroup analysis based on structured mixed-effects models for longitudinal data.

Juan Shen1, Annie Qu2

  • 1Department of Statistics, Fudan University , Shanghai, China.

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|March 5, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new structured mixed-effects model for longitudinal data to identify subgroups and their characteristics over time. The proposed method enhances subgroup analysis for time-course data, outperforming existing approaches.

Keywords:
EM algorithmheterogeneous componentsmixed-effects modelsmixture modelsubgroup identification

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Subgroup analysis is crucial for identifying distinct patient groups.
  • Existing methods for subgroup analysis are limited for longitudinal data.
  • Understanding subgroup membership over time is essential for personalized medicine.

Purpose of the Study:

  • To develop a novel structured mixed-effects model for longitudinal data.
  • To simultaneously model subgroup distribution and identify subgroup membership.
  • To address the under-studied area of subgroup analysis in longitudinal studies.

Main Methods:

  • Proposed a structured mixed-effects approach for longitudinal data.
  • Modeled heterogeneous treatment effects using a two-component mixture model.
  • Incorporated subgroup membership via a logistic model with covariates.
  • Utilized an EM-type algorithm for parameter estimation, ensuring time-invariant subgroup membership.

Main Results:

  • The proposed model effectively identifies subgroup membership in longitudinal data.
  • Numerical studies and a real data example validated the model's performance.
  • The method demonstrated superior performance compared to competing approaches.

Conclusions:

  • The structured mixed-effects model offers a robust framework for longitudinal subgroup analysis.
  • This approach enables simultaneous identification of subgroups and estimation of time-invariant treatment effects.
  • The findings advance the application of subgroup analysis in understanding complex longitudinal data patterns.