摘要：来自斯坦福大学医学院和艾奥尼纳大学医学院的两名教授开展了元分析（meta-analysis），发现80%以上的高引用率生物标志物研究过度报告了生物标志物与疾病的关联，该项成果发表在近期的《美国医学会志》（JAMA）上。
近年来，多个生物标志物被高引用率研究所提出，作为疾病风险、预后或对治疗反应的决定因素，但最终只有很少转化成临床实践。因此，斯坦福大学医学院斯坦福预防研究中心的John Ioannidis和艾奥尼纳大学医学院的Orestis Panagiotou教授联合开展了这项研究。
他们搜索了ISI Web of Science和MEDLINE，收集了2010年12月以前引用率超过400且在高引用率生物医学杂志上发表的文章。通过对35个高引用率的生物标志物关联研究的评估，他们发现86%（30个）的研究过高估计了某一生物标志物的影响。
Ioannidis和Panagiotou认为，临床医生应当更加注意这些生物标志物关联研究，之后才能将基于这些高引用率研究的新程序融入他们的实践中。这些高引用率但夸大的生物标志物关联可能误导医生在诊断过程中使用，但是却不能改善患者的结果。
例如，在2006年，前杜克大学癌症研究人员Anil Potti及同事在《新英格兰医学杂志》上发表了一篇文章，根据基因表达谱构建了一个计算机模型，能预测肿瘤对化疗的敏感性。后来，Potti开展了两项以此研究为基础的临床试验。然而，其他研究人员无法重复出这些结果。因此，到2010年底，临床试验被暂停，而到2011年，这篇文章也被撤回。
杜克转化医学研究所的主任Robert Califf相信这样的例子不止是生物标志物的领域。他认为人们倾向于报告极端的结果，有时候会发表假阳性的数据。
为了降低假阳性率，Califf认为研究人员在数据来源上应当有更高的透明度。研究的相关文件应当放在网上，包括所有原始数据，生成最终分析的步骤，以及所使用软件的代码。有了这些信息，研究人员可独立做出对结论的判定。
生物探索推荐英文论文摘要：
Comparison of Effect Sizes Associated With Biomarkers Reported in Highly Cited Individual Articles and in Subsequent Meta-analyses
Abstract
Context Many biomarkers are proposed in highly cited studies as determinants of disease risk, prognosis, or response to treatment, but few eventually transform clinical practice.
Objective To examine whether the magnitude of the effect sizes of biomarkers proposed in highly cited studies is accurate or overestimated.
Data Sources We searched ISI Web of Science and MEDLINE until December 2010.
Study Selection We included biomarker studies that had a relative risk presented in their abstract. Eligible articles were those that had received more than 400 citations in the ISI Web of Science and that had been published in any of 24 highly cited biomedical journals. We also searched MEDLINE for subsequent meta-analyses on the same associations (same biomarker and same outcome).
Data Extraction In the highly cited studies, data extraction was focused on the disease/outcome, biomarker under study, and first reported relative risk in the abstract. From each meta-analysis, we extracted the overall relative risk and the relative risk in the largest study. Data extraction was performed independently by 2 investigators.
Results We evaluated 35 highly cited associations. For 30 of the 35 (86%), the highly cited studies had a stronger effect estimate than the largest study; for 3 the largest study was also the highly cited study; and only twice was the effect size estimate stronger in the largest than in the highly cited study. For 29 of the 35 (83%) highly cited studies, the corresponding meta-analysis found a smaller effect estimate. Only 15 of the associations were nominally statistically significant based on the largest studies, and of those only 7 had a relative risk point estimate greater than 1.37.
Conclusion Highly cited biomarker studies often report larger effect estimates for postulated associations than are reported in subsequent meta-analyses evaluating the same associations.
KEYWORDS: BIOLOGICAL MARKERS, DATA INTERPRETATION, STATISTICAL, EFFECT MODIFIERS (EPIDEMIOLOGY), META-ANALYSIS AS TOPIC, PREDICTIVE VALUE OF TESTS, RISK ASSESSMENT, SAMPLE SIZE.
