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September 2020 Statistical methods for analysis of combined categorical biomarker data from multiple studies
Chao Cheng, Molin Wang
Ann. Appl. Stat. 14(3): 1146-1163 (September 2020). DOI: 10.1214/20-AOAS1337

Abstract

In the analysis of pooled data from multiple studies involving a biomarker exposure, the biomarker measurements can vary across laboratories and usually require calibration to a reference assay prior to pooling. Previous researches consider the measurements from a reference laboratory as the gold standard, even though measurements in the reference laboratory are not necessarily closer to the underlying truth in reality. In this paper we do not treat any laboratory measurements as the gold standard, and we develop two statistical methods, the exact calibration and cut-off calibration methods, for the analysis of aggregated categorical biomarker data. We compare the performance of both methods for estimating the biomarker-disease relationship under a random sample or controls-only calibration design. Our findings include: (1) the exact calibration method provides significantly less biased estimates and more accurate confidence intervals than the other method; (2) the cut-off calibration method could yield estimates with minimal bias and valid confidence intervals under small measurement errors and/or small exposure effects; (3) controls-only calibration design can result in additional bias, but the bias is minimal if the exposure effects and/or disease prevalences are small. Finally, we illustrate the methods in an application evaluating the relationship between circulating vitamin D levels and colorectal cancer risk in a pooling project.

Citation

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Chao Cheng. Molin Wang. "Statistical methods for analysis of combined categorical biomarker data from multiple studies." Ann. Appl. Stat. 14 (3) 1146 - 1163, September 2020. https://doi.org/10.1214/20-AOAS1337

Information

Received: 1 April 2019; Revised: 1 March 2020; Published: September 2020
First available in Project Euclid: 18 September 2020

MathSciNet: MR4152127
Digital Object Identifier: 10.1214/20-AOAS1337

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.14 • No. 3 • September 2020
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