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A repeated measures approach to pooled and calibrated biomarker data
Abigail Sloan1, Chao Cheng2, Bernard Rosner1
1Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
Biometrics
|December 30, 2021
Summary
This study introduces a new method for calibrating biomarker data from multiple studies, improving precision for meta-analyses. The repeated measures approach offers valid inference for biomarker associations, like vitamin D and stroke risk.
Area of Science:
- Biostatistics
- Epidemiology
- Biomarker Analysis
Background:
- Participant-level meta-analysis enhances statistical power and enables subgroup analyses.
- Biomarker data from diverse studies require calibration to address laboratory and assay variability.
- Existing calibration methods often rely on measurement error techniques and imputation.
Approach:
- Propose a novel repeated measures method for calibrating multi-study biomarker data.
- Incorporate study-specific calibration subsets and account for correlated measurements.
- Compare the repeated measures approach with traditional measurement error techniques.
Key Points:
- The repeated measures method effectively calibrates biomarker measurements across studies.
- This approach accounts for within-person correlation and laboratory-specific variations.
- Valid statistical inference is achieved for pooled biomarker analyses.
Conclusions:
- The proposed repeated measures calibration method provides a robust alternative to existing techniques.
- This method enhances the reliability of biomarker associations in large-scale meta-analyses.
- Demonstrated utility in analyzing the association between vitamin D and stroke.
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