Effect of formal statistical significance on the credibility of observational associations
Effect of formal statistical significance on the credibility of observational associations
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DOI：
10.1093/aje/kwn156
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发表时间：
2008-08-15
影响因子：
5
通讯作者：
Ioannidis, John P. A.
中科院分区：
文献类型：
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作者：
Ioannidis, John P. A.
作者评估了名义统计显著性对改变大量观察性关联中零假设与备择假设的可信度的影响，这些观察性关联声称具有正式的统计显著性（p < 0.05）。对2004-2005年发表的272项观察性关联和50项关于基因-疾病关联的荟萃分析（752项研究）的数据集进行了不同假设下的贝叶斯因子（B）计算，这些数据集具有统计学意义。有显著性差异（P < 0.05）。根据先前的公式，统计学显著性结果为不同风险因素的272种流行病学关联中的54-77%和遗传荟萃分析的50种关联中的44- 70%的可信度（B > 0.10）提供了不太强的支持。有时名义上有统计学意义。与研究之前的想法相比，cant结果甚至降低了探索关联的可信度。6项荟萃分析中有5项（B > 0.032）的支持度低于实质性支持度，在随后（最近）的荟萃分析中失去了名义统计学显著性，而7项具有决定性支持度的荟萃分析中没有出现这种情况（B < 0.01）。在这些大量的观察性关联数据中，单靠形式上的统计显著性并不能增加许多假设性关联的可信度。贝叶斯因子可以常规地用于解释“显著”关联。
The author evaluated the implications of nominal statistical significance for changing the credibility of null versus alternative hypotheses across a large number of observational associations for which formal statistical significance (p < 0.05) was claimed. Calculation of the Bayes factor (B) under different assumptions was performed on 272 observational associations published in 2004-2005 and a data set of 50 meta-analyses on gene-disease associations (752 studies) for which statistically signi. cant associations had been claimed (p < 0.05). Depending on the formulation of the prior, statistically significant results offered less than strong support to the credibility (B > 0.10) for 54-77% of the 272 epidemiologic associations for diverse risk factors and 44-70% of the 50 associations from genetic meta-analyses. Sometimes nominally statistically signi. cant results even decreased the credibility of the probed association in comparison with what was thought before the study was conducted. Five of six meta-analyses with less than substantial support (B > 0.032) lost their nominal statistical significance in a subsequent (more recent) meta-analysis, while this did not occur in any of seven meta-analyses with decisive support (B < 0.01). In these large data sets of observational associations, formal statistical significance alone failed to increase much the credibility of many postulated associations. Bayes factors may be used routinely to interpret "significant'' associations.