A General Linear Mixed Effect Model to Infer Biomarker Correlations by Bridging Retrospectively Measured Data Across Multiple Studies.

ABSTRACT: A major challenge in biomedical research is that large sample sizes are necessary for sufficient statistical power to detect subtle but potentially important associations between biomarkers and clinical outcomes. Large sample sizes can be achieved by combining biomarker data from multiple studies, but because fluid biomarker platforms and imaging protocols often vary across studies, data from different studies must be bridged or harmonized. We conceptualize that, for a biomarker measured by different studies, a true and latent biomarker exists and underlies the different versions of the observed biomarker through a measurement error model. We then examine the true biological correlation of the latent biomarker with a standard clinical outcome by leveraging biomarker values from a subset of

SUBMITTER: Xiong C 

PROVIDER: S-EPMC12621524 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature