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首页> 外文期刊>Prevention science: the official journal of the Society for Prevention Research >Detecting Moderator Effects Using Subgroup Analyses
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Detecting Moderator Effects Using Subgroup Analyses

机译：使用子组分析检测主持人的影响

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摘要

In the analysis of prevention and intervention studies, it is often important to investigate whether treatment effects vary among subgroups of patients defined by individual characteristics. These "subgroup analyses" can provide information about how best to use a new prevention or intervention program. However, subgroup analyses can be misleading if they test data-driven hypotheses, employ inappropriate statistical methods, or fail to account for multiple testing. These problems have led to a general suspicion of findings from subgroup analyses. This article discusses sound methods for conducting subgroup analyses to detect moderators. Multiple authors have argued that, to assess whether a treatment effect varies across subgroups defined by patient characteristics, analyses should be based on tests for interaction rather than treatment comparisons within the subgroups. We discuss the concept of heterogeneity and its dependence on the metric used to describe treatment effects. We discuss issues of multiple comparisons related to subgroup analyses and the importance of considering multiplicity in the interpretation of results. We also discuss the types of questions that would lead to subgroup analyses and how different scientific goals may affect the study at the design stage. Finally, we discuss subgroup analyses based on post-baseline factors and the complexity associated with this type of subgroup analysis.
机译：在预防和干预研究的分析中，通常重要的是要调查治疗效果是否因个体特征而异。这些“亚组分析”可以提供有关如何最好地使用新的预防或干预计划的信息。但是，如果亚组分析测试数据驱动的假设，采用不适当的统计方法或无法说明多次测试，则可能会产生误导。这些问题导致人们普遍怀疑亚组分析的结果。本文讨论了进行子组分析以检测主持人的合理方法。多位作者争辩说，要评估治疗效果是否在由患者特征定义的亚组之间变化，分析应基于相互作用的测试，而不是亚组内的治疗比较。我们讨论了异质性的概念及其对用来描述治疗效果的指标的依赖性。我们讨论与子组分析相关的多重比较问题，以及在结果解释中考虑多重性的重要性。我们还将讨论导致亚组分析的问题类型，以及不同的科学目标如何在设计阶段影响研究。最后，我们讨论基于基线后因素的子组分析以及与此类子组分析相关的复杂性。

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