Design and analysis considerations for combining data from multiple biomarker studies.

ABSTRACT: Pooling data from multiple studies improves estimation of exposure-disease associations through increased sample size. However, biomarker exposure measurements can vary substantially across laboratories and often require calibration to a reference assay prior to pooling. We develop two statistical methods for aggregating biomarker data from multiple studies: the full calibration method and the internalized method. The full calibration method calibrates all biomarker measurements regardless of the availability of reference laboratory measurements while the internalized method calibrates only non-reference laboratory measurements. We compare the performance of these two aggregation methods to two-stage methods. Furthermore, we compare the aggregated and two-stage methods when estimating the

SUBMITTER: Sloan A 

PROVIDER: S-EPMC6755899 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature