Statistical method for pooling categorical biomarkers from multi-center matched/nested case-control studies

TL;DR

This paper introduces a likelihood-based statistical method for analyzing categorical biomarkers in pooled multi-center matched case-control studies, accounting for measurement variability and calibration uncertainties to improve bias reduction.

Contribution

It develops a novel likelihood approach with a sandwich variance estimator to handle calibration errors in pooled categorical biomarker data from multiple studies.

Findings

Method performs well in simulations with various sample sizes.

Application to vitamin D data illustrates practical utility.

Provides valid variance estimates despite calibration uncertainties.

Abstract

Pooled analyses that aggregate data from multiple studies are becoming increasingly common in collaborative epidemiologic research in order to increase the size and diversity of the study population. However, 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 categorical biomarkers 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.