Statistical Methods for Selective Biomarker Testing

A. Adam Ding (), Natalie DelRocco () and Samuel S. Wu ()
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A. Adam Ding: Northeastern University
Natalie DelRocco: University of Florida
Samuel S. Wu: University of Florida

Statistics in Biosciences, 2024, vol. 16, issue 3, No 8, 693-722

Abstract: Abstract Biomarkers are critically important tools in modern clinical diagnosis, prognosis, and classification/prediction. However, there are fiscal and analytical barriers to biomarker research. Selective Genotyping is an approach to increasing study power and efficiency where individuals with the most extreme phenotype (response) are chosen for genotyping (exposure) in order to maximize the information in the sample. In this article, we describe an analogous procedure in the biomarker testing landscape where both response and biomarker (exposure) are continuous. We propose an intuitive reverse-regression least squares estimator for the parameters relating biomarker value to response. An expression for robust standard error and corresponding confidence interval are derived. A simulation study is used to demonstrate that this method is unbiased and efficient relative to estimates from random sampling when the joint normal distribution assumption is met, and to compare the estimator to an alternative under a related sampling design. We illustrate application of proposed methods on data from a chronic pain clinical trial.

Keywords: Extreme sampling; Outcome-dependent sampling; Selective genotyping; Simple linear regression (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12561-023-09416-3

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