Biomarker is a critically important tool 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. Monte Carlo simulations show that this method is unbiased and efficient relative to estimates from random sampling when the joint normal distribution assumption is met. We illustrate application of proposed methods on data from a chronic pain clinical trial.

中文翻译：

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选择性生物标志物测试的统计方法

生物标志物是现代临床诊断、预后和分类/预测中至关重要的工具。然而，生物标志物研究存在财政和分析障碍。选择性基因分型是一种提高研究能力和效率的方法，其中选择具有最极端表型（反应）的个体进行基因分型（暴露），以最大化样本中的信息。在本文中，我们描述了生物标志物测试环境中的类似过程，其中响应和生物标志物（暴露）都是连续的。我们提出了一个直观的反向回归最小二乘估计器，用于将生物标志物值与反应相关的参数。蒙特卡罗模拟表明，当满足联合正态分布假设时，该方法相对于随机抽样的估计是无偏且有效的。