Statistical methods for analysis of combined biomarker data from multiple nested case-control studies.

ABSTRACT: By combining data across multiple studies, researchers increase sample size, statistical power, and precision for pooled analyses of biomarker-disease associations. However, researchers must adjust for between-study variability in biomarker measurements. Previous research often treats the biomarker measurements from a reference laboratory as a gold standard, even though those measurements are certainly not equal to their true values. This paper addresses measurement error and bias arising from both the reference and study-specific laboratories. We develop two calibration methods, the exact calibration method and approximate calibration method, for pooling biomarker data drawn from nested or matched case-control studies, where the calibration subset is obtained by randomly selecting control

SUBMITTER: Cheng C 

PROVIDER: S-EPMC8454258 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature