ArticleInternational journal of epidemiology2015
Standardized binomial models for risk or prevalence ratios and differences.
Article in International journal of epidemiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers, 4 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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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
80 citing papers in PubMed, 4 syntheses or guidelines pooled it.
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- Impact of maternal hepatitis B carrier status on congenital abnormalities: a systematic review and meta-analysis.BMJ open · 2023Pooled it
- Controversy and Debate : Questionable utility of the relative risk in clinical research: Paper 4 :Odds Ratios are far from "portable" - A call to use realistic models for effect variation in meta-analysis.Journal of clinical epidemiology · 2022Pooled it
- Effectiveness of psychological treatments for depression and alcohol use disorder delivered by community-based counsellors: two pragmatic randomised controlled trials within primary healthcare in Nepal.The British journal of psychiatry : the journal of mental science · 2019Trial
- Feasibility and efficacy of shared decision making for first-admission schizophrenia: a randomized clinical trial.BMC psychiatry · 2017Trial
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- The effect of antenatal common mental disorders on infant birth weight at the Dabat health and demographic surveillance site, Northwest Ethiopia: a community-based cohort study.BMC pregnancy and childbirth · 2026Article
- Workplace violence and self-reported physical and mental health: a national cross-sectional study in Lebanon.Journal of global health · 2026Article
- The Role of Self-Classified Race/Ethnicity, Insurance Status, and Hospital Type in Benign Hysterectomy.Journal of women's health (2002) · 2026Article
- Pregnancy Outcomes in Women With Systemic Sclerosis Before and/or After Diagnosis and by Parity: A Swedish Population-Based Cohort Study.Arthritis & rheumatology (Hoboken, N.J.) · 2026Article
- Association of Diet Quality with Depression, Anxiety, and Comorbidity Symptoms in Chinese School-Aged Children.Nutrients · 2025Article
- Maternal Satisfaction With Universal Postnatal Nurse Home Visiting Services and Its Covariates: A Cross-Sectional Survey of 22,040 Mothers With Young Children in Seoul, Korea.Journal of Korean medical science · 2025Article
- Prognostic impact of p16 and high-risk HPV DNA in ~1300 patients with vulvar cancer.International journal of cancer · 2025Article
- Urban green spaces and mental health: Findings from Uganda.Hygiene and environmental health advances · 2025Article
- Current, persistent, and low-poverty area residence and social risks among Black cancer survivors.Cancer · 2025Article
- Prevalence and determinants of pregnancy termination for childbearing women using the modified Poisson regression model: a cross-sectional study of the Tanzania Demographic and Health Survey (TDHS) 2022.BMC public health · 2025Article
- Article
- Individual and collective positive health behaviors and academic achievement among U.S. undergraduate students.PloS one · 2025Article
20 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
backgroundEpidemiologists often analyse binary outcomes in cohort and cross-sectional studies using multivariable logistic regression models, yielding estimates of adjusted odds ratios. It is widely known that the odds ratio closely approximates the risk or prevalence ratio when the outcome is rare, and it does not do so when the outcome is common. Consequently, investigators may decide to directly estimate the risk or prevalence ratio using a log binomial regression model.
methodsWe describe the use of a marginal structural binomial regression model to estimate standardized risk or prevalence ratios and differences. We illustrate the proposed approach using data from a cohort study of coronary heart disease status in Evans County, Georgia, USA.
resultsThe approach reduces problems with model convergence typical of log binomial regression by shifting all explanatory variables except the exposures of primary interest from the linear predictor of the outcome regression model to a model for the standardization weights. The approach also facilitates evaluation of departures from additivity in the joint effects of two exposures.
conclusionsEpidemiologists should consider reporting standardized risk or prevalence ratios and differences in cohort and cross-sectional studies. These are readily-obtained using the SAS, Stata and R statistical software packages. The proposed approach estimates the exposure effect in the total population.
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