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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Identifying multiple change points in a linear mixed effects model.

Yinglei Lai1, Paul S Albert

  • 1Department of Statistics and Biostatistics Center, The George Washington University, Rome Hall, Room 553, 801 22nd St. N.W., Washington, D.C., 20052, U.S.A.

Statistics in Medicine
|October 12, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical framework using linear mixed effects models to find multiple change points in longitudinal data. The method effectively identifies shifts in data trends, aiding in the analysis of complex health outcomes.

Keywords:
change pointdynamic programming algorithmexpectation-maximization algorithmlinear mixed effects modellongitudinal data

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Change-point analysis is crucial for identifying shifts in data over time.
  • Existing methods for longitudinal data often struggle to detect multiple change points effectively.

Purpose of the Study:

  • To propose a novel linear mixed effects modeling framework for identifying multiple change points in longitudinal Gaussian data.
  • To develop an integrated statistical and computational approach combining expectation-maximization and dynamic programming algorithms.

Main Methods:

  • Development of a linear mixed effects modeling framework.
  • Integration of expectation-maximization and dynamic programming algorithms for computational efficiency.
  • Comprehensive simulation studies to evaluate method performance.

Main Results:

  • The proposed framework successfully identifies multiple change points in simulated longitudinal Gaussian data.
  • The integrated algorithm demonstrates robust performance in detecting these change points.
  • The method is illustrated using real-world data from a Type I diabetes intervention trial.

Conclusions:

  • The developed framework provides an effective tool for detecting multiple change points in longitudinal data.
  • This method enhances the analysis of complex longitudinal outcomes, such as those in clinical trials.
  • The approach offers a valuable advancement in statistical methodology for time-series data analysis.