Biomarker validation, like any other confirmatory process based on statistical methodology, must discern associations that occur by chance from those reflecting true biological relationships. Validity of a biomarker is established by authenticating its correlation with clinical outcome. Validated biomarkers can lead to targeted therapy, improve clinical diagnosis, and serve as useful prognostic and predictive factors of clinical outcome. Statistical concerns such as confounding and multiplicity are common in biomarker validation studies. This article discusses four major areas of concern in the biomarker validation process and some of the proposed solutions. Because present-day statistical packages enable the researcher to address these common concerns, the purpose of this discussion is to raise awareness of these statistical issues in the hope of improving the reproducibility of validation study findings.
摘要
与基于统计方法学的任何其它确认过程一样，生物标志物验证必须对偶尔出现且反映真实生物学关系的关联加以辨别。生物标志物的有效性是通过证实其与临床结果的相关性来确定的。经过验证的生物标志物可以引导靶向疗法，改善临床诊断，并作为临床结果的有用的预后和预测因子。诸如混淆性和多重性等统计学问题在生物标志物验证研究中很常见。本文讨论了生物标志物验证过程的四个主要关注领域以及一些拟议的解决方案。由于当今的统计软件包使研究人员能够处理这些常见的关注点，因此本讨论的目的是提高对这些统计问题的认识，以期改善验证研究结果的再现性。(The Oncologist) 2014;19:886–891
Keywords: Biomarker; Confounding factors; Selection bias; Validation studies.
©AlphaMed Press.