ArticleAmerican journal of epidemiology2014
Maximum likelihood, profile likelihood, and penalized likelihood: a primer.
Article in American journal of epidemiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 5 of them syntheses that pooled it.
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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.
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Who cites it
78 citing papers in PubMed, 5 syntheses or guidelines pooled it.
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- Challenges in developing methods for quantifying the effects of weather and climate on water-associated diseases: A systematic review.PLoS neglected tropical diseases · 2017Pooled it
- Potential of the test-negative design for measuring influenza vaccine effectiveness: a systematic review.Expert review of vaccines · 2014Pooled it
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- A Primer on Large-Sample Statistical Inference for Epidemiologists.Epidemiologic methods · 2026Article
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- Sleep and circadian rhythm disturbances as risk and progression factors for multiple chronic overlapping pain conditions: a protocol for a longitudinal study.Pain reports · 2024Article
- Adjustment for sparse data bias in odds ratios: Significance to appraisal of risk of diabetes due to occupational trichlorfon insecticide exposure.Global epidemiology · 2024Article
- Impact of COVID-19 Vaccination in Thailand: Averted Deaths and Severe Infections Across Age Groups.Tropical medicine and infectious disease · 2024Article
18 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
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Abstract
The method of maximum likelihood is widely used in epidemiology, yet many epidemiologists receive little or no education in the conceptual underpinnings of the approach. Here we provide a primer on maximum likelihood and some important extensions which have proven useful in epidemiologic research, and which reveal connections between maximum likelihood and Bayesian methods. For a given data set and probability model, maximum likelihood finds values of the model parameters that give the observed data the highest probability. As with all inferential statistical methods, maximum likelihood is based on an assumed model and cannot account for bias sources that are not controlled by the model or the study design. Maximum likelihood is nonetheless popular, because it is computationally straightforward and intuitive and because maximum likelihood estimators have desirable large-sample properties in the (largely fictitious) case in which the model has been correctly specified. Here, we work through an example to illustrate the mechanics of maximum likelihood estimation and indicate how improvements can be made easily with commercial software. We then describe recent extensions and generalizations which are better suited to observational health research and which should arguably replace standard maximum likelihood as the default method.
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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.