Munafò Marcus R, Flint Jonathan
Cancer Research UK GPRG, Department of Clinical Pharmacology, Radcliffe Infirmary, University of Oxford, Oxford OX2 6HE, UK.
Trends Genet. 2004 Sep;20(9):439-44. doi: 10.1016/j.tig.2004.06.014.
Meta-analysis, a statistical tool for combining results across studies, is becoming popular as a method for resolving discrepancies in genetic association studies. Persistent difficulties in obtaining robust, replicable results in genetic association studies are almost certainly because genetic effects are small, requiring studies with many thousands of subjects to be detected. In this article, we describe how meta-analysis works and consider whether it will solve the problem of underpowered studies or whether it is another affliction visited by statisticians on geneticists. We show that meta-analysis has been successful in revealing unexpected sources of heterogeneity, such as publication bias. If heterogeneity is adequately recognized and taken into account, meta-analysis can confirm the involvement of a genetic variant, but it is not a substitute for an adequately powered primary study.
荟萃分析是一种整合多项研究结果的统计工具，作为解决基因关联研究中差异的一种方法正变得越来越流行。基因关联研究中持续存在难以获得可靠、可重复结果的问题，几乎可以肯定是因为基因效应很小，需要数千名受试者的研究才能检测到。在本文中，我们描述了荟萃分析的工作原理，并思考它是否能解决研究效能不足的问题，或者它是否是统计学家给遗传学家带来的另一个难题。我们表明，荟萃分析已成功揭示了意想不到的异质性来源，如发表偏倚。如果能充分认识并考虑异质性，荟萃分析可以证实基因变异的参与，但它不能替代有足够效能的原始研究。
