ReviewEpidemiology (Cambridge, Mass.)2011
Designs for the combination of group- and individual-level data.
Review in Epidemiology (Cambridge, Mass.), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Examining the Reliability of Model-Based Meta-Analysis (MBMA) Outcomes: A Simulation Study.CPT: pharmacometrics & systems pharmacology · 2026Article
- Utilizing Mobile Health Technology to Enhance Brace Compliance: Feasibility and Effectiveness of an App-Based Monitoring System for Adolescents with Idiopathic Scoliosis.Journal of personalized medicine · 2025Article
- Socioeconomic position and urban environments as drivers of antimicrobial resistance? An ecological study in Germany, 2010 to 2019.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2025Article
- State-level immigrant policies and diabetes prevalence in Latino and Asian American groups: a weighted multilevel analysis.BMJ public health · 2025Article
- Effect of community attitudes on suicide mortality in South Korea: a nationwide ecological study.Frontiers in psychiatry · 2024Article
- A flexible hierarchical framework for improving inference in area-referenced environmental health studies.Biometrical journal. Biometrische Zeitschrift · 2020Article
- Predictive models of diabetes complications: protocol for a scoping review.Systematic reviews · 2020Article
- Exergaming With Integrated Head Turn Tasks Improves Compensatory Saccade Pattern in Some Patients With Chronic Peripheral Unilateral Vestibular Hypofunction.Frontiers in neurology · 2020Article
- An Assessment of Environmental Health Measures in the Deepwater Horizon Research Consortia.Current opinion in toxicology · 2019Article
- Understanding and Mitigating the Replication Crisis, for Environmental Epidemiologists.Current environmental health reports · 2019Review
- On the Analysis of Case-Control Studies in Cluster-correlated Data Settings.Epidemiology (Cambridge, Mass.) · 2018Article
- Traffic-Related Air Pollution and All-Cause Mortality during Tuberculosis Treatment in California.Environmental health perspectives · 2017Article
- Individual level covariate adjusted conditional autoregressive (indiCAR) model for disease mapping.International journal of health geographics · 2016Article
- Strategies for monitoring and evaluation of resource-limited national antiretroviral therapy programs: the two-phase design.BMC medical research methodology · 2015Article
- Article
- Effect of HSV-2 on population-level trends in HIV incidence in Uganda between 1990 and 2007.Tropical medicine & international health : TM & IH · 2013Article
- Comparison of individual-level versus area-level socioeconomic measures in assessing health outcomes of children in Olmsted County, Minnesota.Journal of epidemiology and community health · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundStudies of ecologic or aggregate data suffer from a broad range of biases when scientific interest lies with individual-level associations. To overcome these biases, epidemiologists can choose from a range of designs that combine these group-level data with individual-level data. The individual-level data provide information to identify, evaluate, and control bias, whereas the group-level data are often readily accessible and provide gains in efficiency and power. Within this context, the literature on developing models, particularly multilevel models, is well-established, but little work has been published to help researchers choose among competing designs and plan additional data collection.
methodsWe review recently proposed "combined" group- and individual-level designs and methods that collect and analyze data at 2 levels of aggregation. These include aggregate data designs, hierarchical related regression, two-phase designs, and hybrid designs for ecologic inference.
resultsThe various methods differ in (i) the data elements available at the group and individual levels and (ii) the statistical techniques used to combine the 2 data sources. Implementing these techniques requires care, and it may often be simpler to ignore the group-level data once the individual-level data are collected. A simulation study, based on birth-weight data from North Carolina, is used to illustrate the benefit of incorporating group-level information.
conclusionsOur focus is on settings where there are individual-level data to supplement readily accessible group-level data. In this context, no single design is ideal. Choosing which design to adopt depends primarily on the model of interest and the nature of the available group-level data.
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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.