Ntzani Evangelia E, Khoury Muin J, Ioannidis John P A
University of Ioannina, School of Medicine, Ioannina, Greece.
IARC Sci Publ. 2011(163):323-36.
The rapidly growing number of molecular epidemiology studies is providing an enormous, often multidimensional, body of evidence on the association of various disease outcomes and biomarkers. The testing and validation of statistical hypotheses in genetic and molecular epidemiology presents a major challenge requiring methodological rigor and analytical power. The non-replication of many genetic and other biomarker association studies suggests that there may be an abundance of spurious findings in the field. This chapter will discuss ways of combining evidence from different sources using meta-analysis methods. Research synthesis not only aims at producing a summary effect estimate for a specific biomarker, but also offers a unique opportunity for a meticulous attempt to critically appraise a research field, identify substantial differences between or within studies, and detect sources of bias. Systematic reviews and meta-analyses in human genome epidemiology are specifically discussed, as they comprise the bulk of the available evidence in molecular epidemiology where these methods have been applied to date. Considered here are issues regarding validity and interpretation in genetic association studies, as well as strategies for developing and integrating evidence through international consortia. Finally, there is a brief look at how combining data through meta-analysis may be applied in other areas of molecular epidemiology.
分子流行病学研究数量的迅速增长，正在提供大量往往是多维度的证据，用以证明各种疾病结局与生物标志物之间的关联。在遗传和分子流行病学中，统计假设的检验与验证是一项重大挑战，需要方法的严谨性和分析能力。许多遗传及其他生物标志物关联研究无法重复，这表明该领域可能存在大量虚假研究结果。本章将讨论使用荟萃分析方法整合不同来源证据的方式。研究综合不仅旨在得出特定生物标志物的汇总效应估计值，还提供了一个独特的机会，可对一个研究领域进行细致入微的批判性评估，识别研究之间或研究内部的实质性差异，并检测偏差来源。特别讨论了人类基因组流行病学中的系统评价和荟萃分析，因为它们构成了分子流行病学现有证据的主体，到目前为止这些方法已应用于该领域。这里考虑了遗传关联研究中的有效性和解释问题，以及通过国际合作联盟来开发和整合证据的策略。最后，简要探讨了如何通过荟萃分析合并数据，以应用于分子流行病学的其他领域。
