Multiple inferences using confidence intervals
Multiple inferences using confidence intervals
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DOI：
10.1046/j.1440-1681.2000.03223.x
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发表时间：
2000-03-01
影响因子：
2.9
通讯作者：
Ludbrook, J
中科院分区：
文献类型：
--
作者：
Ludbrook, J
1.在最近的一篇评论文章中，由于在实验单元组之间或实验结果之间进行多重比较而导致的假阳性推断问题得到了解决。得出的结论是，最普遍适用的解决方案是使用Ryan-Holm降压Bonferroni程序来控制族明智的（实验明智的）1型错误率。该程序包括调整假设检验产生的P值。它允许假设之间的相关性，并已通过蒙特卡罗模拟验证。这是一个简单的过程，可以用手来执行。3.然而，一些研究人员更喜欢估计效应量，并通过置信区间的方式进行推断，而不是，或除了通过P值的方式来检验假设，这是一些生物医学期刊编辑的政策，坚持这一点。一般认为，如果在一次实验中根据置信区间作出多个推论，则置信区间（如P值）必须进行调整。在本审查中，它示出了如何置信区间可以调整的扩展的Ryan-Holm降压Bonferroni程序的多重性。这可以在连续变量的情况下对组平均值之间的差异进行分析，在分类变量的情况下对比值比或相对风险进行分析，如2 x 2表所示。
1. In a recent review article, the problem of making false-positive inferences as a result of making multiple comparisons between groups of experimental units or between experimental outcomes was addressed.2. It was concluded that the most universally applicable solution was to use the Ryan-Holm step-down Bonferroni procedure to control the family-wise (experiment-wise) type 1 error rate. This procedure consists of adjusting the P values resulting from hypothesis testing. It allows for correlation among hypotheses and has been validated by Monte Carlo simulation. It is a simple procedure and can be performed by hand.3. However, some investigators prefer to estimate effect sizes and make inferences by way of confidence intervals rather than, or in addition to, testing hypotheses by way of P values and it is the policy of some editors of biomedical journals to insist on this. It is not generally recognized that confidence intervals, like P values, must he adjusted if multiple inferences are made from confidence intervals in a single experiment.4. In the present review, it is shown how confidence intervals can be adjusted for multiplicity by an extension of the Ryan-Holm step-down Bonferroni procedure. This can be done for differences between group means in the case of continuous variables and for odds ratios or relative risks in the case of categorical variables set out as 2 x 2 tables.