Statistical method for pooling categorical biomarker data from multi-center matched/nested case-control studies.

Pooled analyses that aggregate data from multiple studies are becoming increasingly common in collaborative epidemiologic research to increase sample size and population diversity. Many biomarkers, such as vitamin D, are routinely analyzed using clinically defined categories rather than their original continuous scales, yet statistical methods for pooling biomarker data across studies have largely focused on continuous biomarkers. Biomarker measurements from different studies are subject to systematic measurement errors and directly pooling them for analyses may lead to biased estimates of the regression parameters. Therefore, study-specific calibration processes must be incorporated in the statistical analyses to address between-study/assay/laboratory variability in the biomarker measurements. We propose a likelihood-based method to evaluate biomarker-disease relationships for biomarkers that are continuously measured but analyzed categorically in matched/nested case-control studies. To account for the additional uncertainties from the calibration processes, we propose a sandwich variance estimator to obtain valid asymptotic variances of the estimated regression parameters. Extensive simulation studies with varying sample sizes and biomarker-disease associations are used to evaluate the finite sample performance of our proposed methods. As an illustration, we apply the methods to a vitamin D pooling project of colorectal cancer to evaluate the effect of categorical vitamin D levels on colorectal cancer risks.