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. 2024 Feb 14;53(2):dyae030.
doi: 10.1093/ije/dyae030.

M-estimation for common epidemiological measures: introduction and applied examples

Rachael K Ross  1 , Paul N Zivich  2   3 , Jeffrey S A Stringer  4 , Stephen R Cole  3
Affiliations

M-estimation for common epidemiological measures: introduction and applied examples

Rachael K Ross et al. Int J Epidemiol. .

Abstract

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.

Keywords: M-estimation; data fusion; estimating equations; logistic regression; standardization.

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Conflict of interest statement

None declared.

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