Artificial effect-size magnification (ESM) may occur in underpowered studies, where effects are reported only because they or their associated p-values have passed some threshold. Ioannidis (2008, Epidemiology 19: 640–648) and Gelman and Carlin (2014, Perspectives on Psychological Science 9: 641–651) have suggested that the plausibility of findings for a specific study can be evaluated by computation of ESM, which requires statistical simulation. In this article, we present a new command called emagnification that allows straightforward implementation of such simulations in Stata. The commands automate these simulations for epidemiological studies and enable the user to assess ESM routinely for published studies using user-selected, study-specific inputs that are commonly reported in published literature. The intention of the command is to allow a wider community to use ESMs as a tool for evaluating the reliability of reported effect sizes and to put an observed statistically significant effect size into a fuller context with respect to potential implications for study conclusions.

人工效应大小放大 (ESM) 可能发生在动力不足的研究中，其中报告效应仅是因为它们或它们相关的p值已超过某个阈值。Ioannidis (2008, Epidemiology 19: 640–648) 和 Gelman and Carlin (2014, Perspectives on Psychological Science 9: 641–651) 建议可以通过计算 ESM 来评估特定研究结果的合理性，这需要统计模拟。在本文中，我们介绍了一个名为emagnification的新命令这允许在 Stata 中直接实现此类模拟。这些命令使流行病学研究的这些模拟自动化，并使用户能够使用用户选择的、特定于研究的输入（通常在已发表的文献中报告）对已发表的研究进行常规的 ESM 评估。该命令的目的是让更广泛的社区使用 ESM 作为评估报告效应量可靠性的工具，并将观察到的统计显着效应量放入更全面的背景中，以了解对研究结论的潜在影响。