Consequences of ignoring clustering in linear regression.
Background: Clustering of observations is a common phenomenon in epidemiological and clinical research. Previous studies have highlighted the importance of using multilevel analysis to account for such clustering, but in practice, methods ignoring clustering are often employed. We used simulated dat...
| Publicado en: | BMC Medical Research Methodology Vol. 21; no. 1; pp. 1 - 14 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
7/7/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151289210&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151289210 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14712288 1CI1 jtl: BMC Medical Research Methodology issn: 14712288 maglogo: N pubinfo: dt: 7/7/2021 vid: 21 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 151289210 151289210 NLM34233609 151289210 10.1186/s12874-021-01333-7 NLM34233609 151289210 ppf: 1 ppct: 13 formats: tig: atl: Consequences of ignoring clustering in linear regression. aug: au: Ntani, Georgia Inskip, Hazel Osmond, Clive Coggon, David affil: Medical Research Council Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK sug: subj: Models, Statistical Computer Simulation Regression Linear Regression Cluster Analysis Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales ab: Background: Clustering of observations is a common phenomenon in epidemiological and clinical research. Previous studies have highlighted the importance of using multilevel analysis to account for such clustering, but in practice, methods ignoring clustering are often employed. We used simulated data to explore the circumstances in which failure to account for clustering in linear regression could lead to importantly erroneous conclusions.Methods: We simulated data following the random-intercept model specification under different scenarios of clustering of a continuous outcome and a single continuous or binary explanatory variable. We fitted random-intercept (RI) and ordinary least squares (OLS) models and compared effect estimates with the "true" value that had been used in simulation. We also assessed the relative precision of effect estimates, and explored the extent to which coverage by 95% confidence intervals and Type I error rates were appropriate.Results: We found that effect estimates from both types of regression model were on average unbiased. However, deviations from the "true" value were greater when the outcome variable was more clustered. For a continuous explanatory variable, they tended also to be greater for the OLS than the RI model, and when the explanatory variable was less clustered. The precision of effect estimates from the OLS model was overestimated when the explanatory variable varied more between than within clusters, and was somewhat underestimated when the explanatory variable was less clustered. The cluster-unadjusted model gave poor coverage rates by 95% confidence intervals and high Type I error rates when the explanatory variable was continuous. With a binary explanatory variable, coverage rates by 95% confidence intervals and Type I error rates deviated from nominal values when the outcome variable was more clustered, but the direction of the deviation varied according to the overall prevalence of the explanatory variable, and the extent to which it was clustered.Conclusions: In this study we identified circumstances in which application of an OLS regression model to clustered data is more likely to mislead statistical inference. The potential for error is greatest when the explanatory variable is continuous, and the outcome variable more clustered (intraclass correlation coefficient is ≥ 0.01). pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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