Multiple testing in disease mapping and descriptive epidemiology
Multiple testing in disease mapping and descriptive epidemiology
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DOI：
10.4081/gh.2010.202
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发表时间：
2010-05-01
影响因子：
1.7
通讯作者：
Biggeri, Annibale
中科院分区：
文献类型：
--
作者：
Catelan, Dolores;Biggeri, Annibale
多重检测的问题在疾病绘图或描述性流行病学中很少得到解决。当分析大量小区域或疾病时，这个问题是相关的。避免了对族错误率 (FWER) 的控制，例如通过 Bonferroni 校正，因为它会导致统计功效的损失。为了克服这些困难，在临床试验和基因组数据分析的背景下，提出了对错误发现率（FDR）的控制，即所有被拒绝的假设中错误拒绝的预期比例。 FDR 有贝叶斯解释，它是所谓 q 值（p 值的贝叶斯对应项）的基础。在目前的工作中，我们解决了疾病绘图中的多重性问题，并通过两个真实示例和小型模拟研究展示了 FDR 方法的性能。这些示例考虑测试给定区域的多种疾病或给定疾病的多个区域。使用未调整的 p 值进行多次测试，可以识别出大量风险改变的区域或疾病，而 FDR 程序是适当的，并且比使用 Bonferroni 校正的 FWER 控制更有效。我们的结论是，FDR 方法足以筛查高/低风险区域或疾病过剩/不足，并且可作为点估计和置信区间的补充程序。
The problem of multiple testing is rarely addressed in disease mapping or descriptive epidemiology. This issue is relevant when a large number of small areas or diseases are analysed. Control of the family wise error rate (FWER), for example via the Bonferroni correction, is avoided because it leads to loss of statistical power. To overcome such difficulties, control of the false discovery rate (FDR), the expected proportion of false rejections among all rejected hypotheses, was proposed in the context of clinical trials and genomic data analysis. FDR has a Bayesian interpretation and it is the basis of the so called q-value, the Bayesian counterpart of the p-value. In the present work, we address the multiplicity problem in disease mapping and show the performance of the FDR approach with two real examples and a small simulation study. The examples consider testing multiple diseases for a given area or multiple areas for a given disease. Using unadjusted p-values for multiple testing, an inappropriately large number of areas or diseases at altered risk are identified, whilst FDR procedures are appropriate and more powerful than the control of the FWER with the Bonferroni correction. We conclude that the FDR approach is adequate to screen for high/low risk areas or for disease excess/deficit and useful as a complementary procedure to point estimates and confidence intervals.