AI 中文总结
研究多元宇宙分析中如何控制假阳性结果，通过在广义线性模型中使用E值，比较通用E值、软秩E值和p到E校准几种方法，发现p到E校准在统计功效上表现优，但样本量或效应量不足时功效适中，还通过应用揭示了相关关联。
AI 中文摘要
多元宇宙分析指的是一种常见情形，即人们希望评估多种可能的治疗定义与多种可能的结果定义之间的关联，可能在多个亚群体中以及其他可能的分析规范中进行。多元宇宙分析是评估所考虑规范间异质性的有用探索工具，但有时也用于评估统计显著性。在后一种情况下，必须认识到正在进行多重比较，并确保对假阳性结果进行有效的统计控制。我们研究在广义线性模型中使用E值作为控制错误发现率的工具，无论所执行的多重分析的依赖结构如何，同时考虑混杂协变量。我们比较了几种方法的性能：通用E值、软秩E值和p到E校准。我们发现，对于多元宇宙分析中通常遇到的问题特征，p到E校准在统计功效方面显著优于其他两种方法，但除非样本量或效应量足够大，否则该功效可能适中。一项研究青少年技术使用与心理健康之间关联的应用揭示了抑郁、低自尊和同伴问题与互联网和社交媒体使用之间的关联。
英文摘要
Multiverse analysis refers to a common situation where one wishes to assess the association between multiple possible treatment definitions and multiple possible outcome definitions, potentially within multiple sub-populations, among other possible analysis specifications. Multiverse analysis is a useful exploratory tool to assess heterogeneity across the considered specifications, but it is sometimes also used to assess statistical significance. In the latter case, it is critical to acknowledge that multiple comparisons are being performed, and to ensure a valid statistical control of false positive findings. We study the use of e-values within generalized linear models as a tool to control the false discovery rate regardless of the dependence structure of the multiple analyses being performed, while accounting for confounding covariates. We compare the performance of several approaches: universal e-values, soft-rank e-values, and p-to-e calibration. We find that, for problem characteristics typically encountered in multiverse analyses, p-to-e calibration significantly outperforms the other two approaches in terms of statistical power, but said power may be moderate unless the sample size or effect sizes are large enough. An application studying the association between teenager technology use and mental well-being reveals association between depression, low self-steem and peer problems with internet and social media usage.
