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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Related Experiment Video

Updated: Sep 14, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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A General Linear Mixed Effect Model to Infer Biomarker Correlations by Bridging Retrospectively Measured Data Across

Chengjie Xiong1,2, Ruijin Lu1,2, David Wolk3

  • 1Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA.

Statistics in Medicine
|July 21, 2025
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Summary
This summary is machine-generated.

Biomedical research can combine multi-study biomarker data by harmonizing platforms. A new meta-analytic model estimates true biological correlations, improving statistical power for detecting associations between biomarkers and clinical outcomes.

Keywords:
Alzheimer disease (AD)fisher transformationgeneral linear mixed modelintraclass correlation coefficient (ICC)random effect

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Area of Science:

  • Biostatistics
  • Biomarker Discovery
  • Clinical Research Methodology

Background:

  • Large sample sizes are crucial in biomedical research for detecting subtle biomarker-outcome associations.
  • Combining data from multiple studies enhances statistical power but requires harmonizing heterogeneous biomarker data.
  • Existing methods struggle with varying platforms and protocols across studies.

Purpose of the Study:

  • To develop a novel meta-analytic approach for harmonizing retrospective biomarker data across studies.
  • To estimate the true biological correlation between latent biomarkers and clinical outcomes.
  • To provide guidance on optimal bridging sample sizes for data harmonization.

Main Methods:

  • Conceptualized a latent biomarker underlying observed versions using a measurement error model.
  • Developed a general linear mixed-effects model integrating correlations and intraclass correlation coefficients (ICC).
  • Incorporated random effects to account for study heterogeneity and used bridging samples for estimation.

Main Results:

  • The proposed model accurately estimates biological correlations with minimal bias (≤0.03).
  • Effective harmonization is achievable even with small to mediocre ICCs.
  • With large ICCs, only 10% bridging samples are needed for unbiased correlation estimates.

Conclusions:

  • The meta-analytic model offers a robust method for harmonizing retrospective biomarker data.
  • It provides valuable guidance on determining the necessary number of bridging samples.
  • The methodology can also address batch effects within single studies.