Assessing subgroup effects with binary data: can the use of different effect measures lead to different conclusions?
Assessing subgroup effects with binary data: can the use of different effect measures lead to different conclusions?
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DOI：
10.1186/1471-2288-5-15
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发表时间：
2005-04-29
影响因子：
4
通讯作者：
Elbourne, Diana
中科院分区：
文献类型：
--
作者：
White, Ian R;Elbourne, Diana
背景：为了使用随机试验的结果，有必要了解观察到的总体获益或危害是否适用于所有个体，或者某些亚组是否比其他亚组获益或危害更多。这个决定通常由交互作用的统计测试来指导。然而，对于二元结果，不同的效应测量产生不同的相互作用测试。例如，英国髋关节试验探讨了怀疑髋关节发育不良的婴儿超声检查对后续髋关节治疗发生的影响。以临床怀疑程度定义的亚组之间的风险比相似（P = 0.14），但亚组之间的优势比和风险差异有很大差异（P < 0.001）。讨论：不同效应测量的相互作用检验不同，因为它们检验不同的零假设。图形技术表明，当亚组风险显著不同时，差异就会出现。我们认为，相互作用的检验是对试验结果对所有纳入的亚组的适用性的检验。因此，相互作用的检验应应用于最不可能先验地表现出相互作用的效果测量。我们将举例说明如何做到这一点。摘要：当亚组间二元结局的风险差异很大时，相互作用试验的选择尤为重要。相互作用试验应预先规定，并以临床知识为指导。
BACKGROUND: In order to use the results of a randomised trial, it is necessary to understand whether the overall observed benefit or harm applies to all individuals, or whether some subgroups receive more benefit or harm than others. This decision is commonly guided by a statistical test for interaction. However, with binary outcomes, different effect measures yield different interaction tests. For example, the UK Hip trial explored the impact of ultrasound of infants with suspected hip dysplasia on the occurrence of subsequent hip treatment. Risk ratios were similar between subgroups defined by level of clinical suspicion (P = 0.14), but odds ratios and risk differences differed strongly between subgroups (P < 0.001).DISCUSSION: Interaction tests on different effect measures differ because they test different null hypotheses. A graphical technique demonstrates that the difference arises when the subgroup risks differ markedly. We consider that the test of interaction acts as a check on the applicability of the trial results to all included subgroups. The test of interaction should therefore be applied to the effect measure which is least likely a priori to exhibit an interaction. We give examples of how this might be done.SUMMARY: The choice of interaction test is especially important when the risk of a binary outcome varies widely between subgroups. The interaction test should be pre-specified and should be guided by clinical knowledge.