A repeated measures approach to pooled and calibrated biomarker data |
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Authors: | Abigail Sloan Chao Cheng Bernard Rosner Regina G Ziegler Stephanie A Smith-Warner Molin Wang |
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Institution: | 1. Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts;2. Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, Connecticut;3. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland;4. Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, Massachusetts |
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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. |
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Keywords: | between-study variability calibration case-control study participant-level meta-analysis pooling |
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