ArticleInternational journal of epidemiology2024
M-estimation for common epidemiological measures: introduction and applied examples.
Article in International journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Confidence regions for multiple outcomes, effect modifiers, and other multiple comparisons.American journal of epidemiology · 2026Article
- The Rogan-Gladen estimator for outcome misclassification.American journal of epidemiology · 2026Article
- Sampling for computational efficiency when conducting analyses in big data.American journal of epidemiology · 2026Article
- Causal Identification Conditions for the Effect of Treatment in the Treated: Illustration Using the Northwest Germany Stroke Registry.Epidemiology (Cambridge, Mass.) · 2026Article
- A Primer on Large-Sample Statistical Inference for Epidemiologists.Epidemiologic methods · 2026Article
- Five Facts About Influence Functions.Epidemiology (Cambridge, Mass.) · 2025Article
- A minimal nontrivial example of higher-order evidence.American journal of epidemiology · 2025Article
- Keep asking: What do I want? What do I have? What do I do? : An approach for combining information to learn about a target population.European journal of epidemiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.