Evidence map›Paper›PMID 39393031›Full record

ArticleNeurology2024

Using Multilevel Models and Generalized Estimating Equation Models to Account for Clustering in Neurology Clinical Research.

Peter C Austin, Moira K Kapral, Manav V Vyas, Jiming Fang, Amy Ying Xin Yu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Peter C AustinFrom the ICES (P.C.A., M.K.K., M.V.V., J.F., A.Y.X.Y.), Toronto; Institute of Health Policy, Management and Evaluation (P.C.A., M.K.K., M.V.V., A.Y.X.Y.), University of Toronto; Sunnybrook Research Institute (P.C.A., A.Y.X.Y.), Toronto; Division of General Internal Medicine (M.K.K.), and Division of Neurology (M.V.V., A.Y.X.Y.), Department of Medicine, University of Toronto, Ontario, Canada.ORCID 0000-0003-3337-233X
Moira K KapralFrom the ICES (P.C.A., M.K.K., M.V.V., J.F., A.Y.X.Y.), Toronto; Institute of Health Policy, Management and Evaluation (P.C.A., M.K.K., M.V.V., A.Y.X.Y.), University of Toronto; Sunnybrook Research Institute (P.C.A., A.Y.X.Y.), Toronto; Division of General Internal Medicine (M.K.K.), and Division of Neurology (M.V.V., A.Y.X.Y.), Department of Medicine, University of Toronto, Ontario, Canada.ORCID 0000-0002-3444-9928
Manav V VyasFrom the ICES (P.C.A., M.K.K., M.V.V., J.F., A.Y.X.Y.), Toronto; Institute of Health Policy, Management and Evaluation (P.C.A., M.K.K., M.V.V., A.Y.X.Y.), University of Toronto; Sunnybrook Research Institute (P.C.A., A.Y.X.Y.), Toronto; Division of General Internal Medicine (M.K.K.), and Division of Neurology (M.V.V., A.Y.X.Y.), Department of Medicine, University of Toronto, Ontario, Canada.ORCID 0000-0002-2953-2540
Jiming FangFrom the ICES (P.C.A., M.K.K., M.V.V., J.F., A.Y.X.Y.), Toronto; Institute of Health Policy, Management and Evaluation (P.C.A., M.K.K., M.V.V., A.Y.X.Y.), University of Toronto; Sunnybrook Research Institute (P.C.A., A.Y.X.Y.), Toronto; Division of General Internal Medicine (M.K.K.), and Division of Neurology (M.V.V., A.Y.X.Y.), Department of Medicine, University of Toronto, Ontario, Canada.ORCID 0000-0002-4526-0987
Amy Ying Xin YuFrom the ICES (P.C.A., M.K.K., M.V.V., J.F., A.Y.X.Y.), Toronto; Institute of Health Policy, Management and Evaluation (P.C.A., M.K.K., M.V.V., A.Y.X.Y.), University of Toronto; Sunnybrook Research Institute (P.C.A., A.Y.X.Y.), Toronto; Division of General Internal Medicine (M.K.K.), and Division of Neurology (M.V.V., A.Y.X.Y.), Department of Medicine, University of Toronto, Ontario, Canada.ORCID 0000-0002-7276-9551

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In clinical and health services research, clustered data (also known as data with a multilevel or hierarchical structure) are frequently encountered. For example, patients may be clustered or nested within hospitals. Understanding when data have a multilevel structure is important because clustering of individuals can induce a homogeneity in outcomes within clusters, so that, even after adjusting for measured covariates, outcomes for 2 individuals in the same cluster are more likely to be similar than outcomes for 2 individuals from different clusters. Using conventional statistical regression models to analyze clustered data can result in incorrect conclusions being drawn. In particular, estimated CIs may be artificially narrow, and significance levels may be artificially low. As a result, one may conclude that there is a statistically significant association when there is none. To avoid this problem, investigators should ensure that their analyses use techniques that account for clustering of data. Generalized linear models estimated using generalized estimating equation (GEE) methods and multilevel regression models (also known as hierarchical regression models, mixed-effects models, or random-effects models) are two such techniques. We provide an introduction to clustered or multilevel data and describe how GEE models or multilevel models can be used for the analysis of multilevel data.

Indexed as

Biomedical ResearchModels, StatisticalMultilevel AnalysisNeurologyCluster AnalysisData Interpretation, StatisticalHumans

Identifiers

PMID39393031
PMCPMC11469681

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.