M-estimation is a statistical procedure that is particularly advantageous for some comon epidemiological analyses, including approaches to estimate an adjusted marginal risk contrast (i.e. inverse probability weighting and g-computation) and data fusion. In such settings, maximum likelihood variance estimates are not consistent. Thus, epidemiologists often resort to bootstrap to estimate the variance. In contrast, M-estimation allows for consistent variance estimates in these settings without requiring the computational complexity of the bootstrap. In this paper, we introduce M-estimation and provide four illustrative examples of implementation along with software code in multiple languages. M-estimation is a flexible and computationally efficient estimation procedure that is a powerful addition to the epidemiologist’s toolbox.

中文翻译：

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常见流行病学指标的 M 估计：介绍和应用实例

M 估计是一种统计程序，对于一些常见的流行病学分析特别有利，包括估计调整后的边际风险对比（即逆概率加权和 g 计算）和数据融合的方法。在这种情况下，最大似然方差估计并不一致。因此，流行病学家经常采用自助法来估计方差。相反，M 估计允许在这些设置中进行一致的方差估计，而不需要引导程序的计算复杂性。在本文中，我们介绍了 M 估计，并提供了四个说明性的实现示例以及多种语言的软件代码。M 估计是一种灵活且计算效率高的估计程序，是流行病学家工具箱的有力补充。