  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.
