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Related Experiment Videos

Trees and tracking

M R Segal1, I B Tager

  • 1Division of Biostatistics, University of California, San Francisco 94143-0560.

Statistics in Medicine
|December 15, 1993
PubMed
Summary
This summary is machine-generated.

This study introduces regression trees as a novel method for assessing how individual characteristics maintain relative rank over time, improving upon existing tracking assessment techniques.

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Epidemiologists use 'tracking' to describe the maintenance of an individual's relative rank for a measured characteristic over time.
  • Assessing tracking requires summarizing individual growth curves and quantifying rank maintenance amidst random error.
  • Previous methodologies for appraising tracking, published in 1981, offered differing approaches.

Purpose of the Study:

  • To introduce a novel methodology for assessing individual attribute tracking using regression trees for longitudinal data.
  • To simultaneously address the challenges of summarizing growth curves and quantifying rank maintenance.
  • To identify subgroups with homogeneous growth curves based on covariates.

Main Methods:

  • Utilizing regression trees for longitudinal data analysis.

Related Experiment Videos

  • Developing a procedure that identifies subgroups defined by covariates.
  • Applying the tree-structured methodology to analyze lung function growth in children.

    • Main Results:

    • The regression tree approach effectively identifies subgroups with homogeneous growth curves.
    • This method simultaneously addresses the summarization of growth curves and the quantification of rank maintenance.
    • An illustrative example demonstrates the application to pediatric lung function growth.

    Conclusions:

    • Regression trees offer a robust and simultaneous approach to assessing longitudinal data tracking.
    • The methodology allows for the identification of specific subgroups with consistent growth patterns.
    • This approach enhances the understanding of how characteristics track over time, particularly in developmental studies.