论文标题
选择性生物标志物测试的统计方法
Statistical Methods for Selective Biomarker Testing
论文作者
论文摘要
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。但是，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）以最大程度地提高样本中的信息。在本文中，我们描述了在生物标志物测试景观中的类似程序，其中反应和生物标志物（暴露）都是连续的。我们提出了与响应有关的参数的直观反向回归估计器。蒙特卡洛模拟表明，当达到关节正态分布假设时，相对于随机抽样的估计，该方法是公正和有效的。我们说明了提出的方法在慢性疼痛临床试验中的数据上的应用。
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.
