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

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...

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Bibliographic Details
Published in:International journal of epidemiology 2024-02, Vol.53 (2)
Main Authors: Ross, Rachael K, Zivich, Paul N, Stringer, Jeffrey S A, Cole, Stephen R
Format: Article
Language:English
Subjects:
Computer Simulation
Epidemiologists
Humans
Language
Models, Statistical
Probability
Software
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Summary: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.
ISSN:0300-5771
1464-3685
1464-3685
DOI:10.1093/ije/dyae030