ArticleStatistics in medicine2018
Measures of clustering and heterogeneity in multilevel Poisson regression analyses of rates/count data.
Article in Statistics in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers.
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Who cites it
110 citing papers in PubMed.
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- Factors associated with admission before the day of elective major surgery: an analysis of data from the UK Perioperative Quality Improvement Programme.British journal of anaesthesia · 2026Observational
- Updated Estimates of Childhood Diarrheal Morbidity in 11 Sub-Saharan African Countries Using the Latest Demographic and Health Surveys (2021-2024): A Multilevel Modified Poisson Regression Analysis.BioMed research international · 2026Article
- Joint statistical analysis of household food insecurity and child stunting in Hawassa Zuria district, Sidama region, Ethiopia.Journal of health, population, and nutrition · 2025Article
- Practice-Level Clustering of Industry Payments to Clinicians.Health services research · 2025Article
- Healthcare resources and differences in kidney disease-related mortality in Italy: a longitudinal study.Journal of nephrology · 2025Article
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- Pooled prevalence of induced abortion and associated factors among reproductive age women in sub-Saharan Africa: a Bayesian multilevel approach.Archives of public health = Archives belges de sante publique · 2025Article
- A matter of measurement? A Swedish register-based study of migrant residential segregation and all-cause mortality.SSM - population health · 2025Article
- Spatial variations and determinants of modern contraceptive utilization among sexually active rural women in Ethiopia using mini EDHS 2019 data: spatial and multilevel analysis.BMC public health · 2025Article
50 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
Abstract
Multilevel data occur frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models. These models incorporate cluster-specific random effects that allow one to partition the total variation in the outcome into between-cluster variation and between-individual variation. The magnitude of the effect of clustering provides a measure of the general contextual effect. When outcomes are binary or time-to-event in nature, the general contextual effect can be quantified by measures of heterogeneity like the median odds ratio or the median hazard ratio, respectively, which can be calculated from a multilevel regression model. Outcomes that are integer counts denoting the number of times that an event occurred are common in epidemiological and medical research. The median (incidence) rate ratio in multilevel Poisson regression for counts that corresponds to the median odds ratio or median hazard ratio for binary or time-to-event outcomes respectively is relatively unknown and is rarely used. The median rate ratio is the median relative change in the rate of the occurrence of the event when comparing identical subjects from 2 randomly selected different clusters that are ordered by rate. We also describe how the variance partition coefficient, which denotes the proportion of the variation in the outcome that is attributable to between-cluster differences, can be computed with count outcomes. We illustrate the application and interpretation of these measures in a case study analyzing the rate of hospital readmission in patients discharged from hospital with a diagnosis of heart failure.
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