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.

翻译：在实际操作中,校准过程涉及从中央实验室和与特定研究“局部”和中央实验室测量有关的适当模型进行随机生物光谱子集的重新分析。该领域以前的工作从测量误差技术的角度处理校准过程,并比照以局部实验室测量为基础的现有测量误差技术,对基于测量误差技术的现有校准方法进行比较。在这项工作中,我们建议了一种反复的措施,用以校准由多项研究结合使用不同实验室和(或)分析得出的生物标记测量方法。我们考虑到在同一人身上进行的测量和在同一实验室进行的测量之间的联系。我们证明,反复测量方法提供了合理的推论,并将它与以测量误差技术为基础的现有校准方法进行比较,例如描述维生素D的流通与中风之间的关联。