A repeated measures approach to pooled and calibrated biomarker data

TL;DR

This paper introduces a repeated measures calibration method for pooled biomarker data from multiple studies, accounting for within-person and within-lab correlations to improve effect estimate accuracy.

Contribution

It proposes a novel repeated measures approach for calibrating pooled biomarker data, addressing correlations and improving inference over existing methods.

Findings

The method provides valid statistical inference.

It outperforms traditional measurement error techniques in simulations.

Applied to vitamin D and stroke data, it yields consistent results.

Abstract

Participant level meta-analysis across multiple studies increases the sample size for pooled analyses, thereby improving precision in effect estimates and enabling subgroup analyses. For analyses involving biomarker measurements as an exposure of interest, investigators must first calibrate the data to address measurement variability arising from usage of different laboratories and/or assays. In practice, the calibration process involves reassaying a random subset of biospecimens from each study at a central laboratory and fitting models that relate the study-specific "local" and central laboratory measurements. Previous work in this area treats the calibration process from the perspective of measurement error techniques and imputes the estimated central laboratory value among individuals with only a local laboratory measurement. In this work, we propose a repeated measures method to calibrate biomarker measurements pooled from multiple studies with study-specific calibration subsets. We account for correlation between measurements made on the same person and between measurements made at the same laboratory. We demonstrate that the repeated measures approach provides valid inference, and compare it to existing calibration approaches grounded in measurement error techniques in an example describing the association between circulating vitamin D and stroke.