ArticleAmerican journal of epidemiology2017
A Multinomial Regression Approach to Model Outcome Heterogeneity.
Article in American journal of epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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
9 citing papers in PubMed.
- Psychological heterogeneity and transitions of non-suicidal self-injury in adolescents: a two-wave longitudinal cohort study.BMC psychiatry · 2026Article
- Article
- Primary Care Asthma Attack Prediction Models for Adults: A Systematic Review of Reported Methodologies and Outcomes.Journal of asthma and allergy · 2024Review
- Risk Factors for the Severity of Complications in Minimally Invasive Total Gastrectomy for Gastric Cancer: a Retrospective Cohort Study.Journal of gastric cancer · 2021Article
- Reflection on modern methods: causal inference considerations for heterogeneous disease etiology.International journal of epidemiology · 2021Article
- Socioeconomic inequalities in low back pain among older people: the JAGES cross-sectional study.International journal for equity in health · 2019Article
- RE: "A MULTINOMIAL REGRESSION APPROACH TO MODEL OUTCOME HETEROGENEITY".American journal of epidemiology · 2018Article
- Inverse-Probability-Weighted Estimation for Monotone and Nonmonotone Missing Data.American journal of epidemiology · 2018Article
- Development and Early Experience of a Primary Care Learning Collaborative in a Large Health Care System.Journal of primary care & community healthArticle
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3 authors.
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Abstract
When a risk factor affects certain categories of a multinomial outcome but not others, outcome heterogeneity is said to be present. A standard epidemiologic approach for modeling risk factors of a categorical outcome typically entails fitting a polytomous logistic regression via maximum likelihood estimation. In this paper, we show that standard polytomous regression is ill equipped to detect outcome heterogeneity and will generally understate the degree to which such heterogeneity may be present. Specifically, nonsaturated polytomous regression will often a priori rule out the possibility of outcome heterogeneity from its parameter space. As a remedy, we propose to model each category of the outcome as a separate binary regression. For full efficiency, we propose to estimate the collection of regression parameters jointly using a constrained Bayesian approach that ensures that one remains within the multinomial model. The approach is straightforward to implement in standard software for Bayesian estimation.
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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.