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.
翻译:生物标志是现代临床诊断、预测和分类/预测中一个极为重要的工具,但是生物标志研究在财政和分析上都存在障碍。选择性基因标志是一种提高研究能力和效率的方法,在这种方法中,选择了最极端苯型(反应)的人来进行基因分类(接触),以尽量扩大样本中的信息。在本条中,我们描述了生物标志测试中一种类似的程序,即反应和生物标志(接触)都是连续的。我们建议了与生物标志值有关的参数的直观反反向最小方方位估计。蒙特卡洛模拟显示,这种方法与满足共同正常分布假设时随机抽样的估计数相比是公正和高效的。我们介绍了对慢性疼痛临床试验数据应用拟议方法的情况。