%0 Journal Article %1 Reboldi2013 %A Reboldi, Gianpaolo %A Angeli, Fabio %A Verdecchia, Paolo %D 2013 %J Cerebrovascular diseases (Basel, Switzerland) %K BiomedicalResearch BiomedicalResearch:statistics&numericaldata CerebrovascularDisorders CerebrovascularDisorders:diagnosis CerebrovascularDisorders:mortality CerebrovascularDisorders:therapy Chi-SquareDistribution ConfoundingFactors(Epidemiology) DataInterpretation Humans LinearModels LogisticModels Models MultivariateAnalysis OddsRatio Prognosis ProportionalHazardsModels RiskFactors Statistical SurvivalAnalysis %N 2 %P 187-93 %R 10.1159/000345491 %T Multivariable analysis in cerebrovascular research: practical notes for the clinician. %U http://www.ncbi.nlm.nih.gov/pubmed/23429297 %V 35 %X The term 'multivariate analysis' is often used when one is referring to a multivariable analysis. 'Multivariate', however, implies a statistical analysis with multiple outcomes. In contrast, multivariable analysis is a statistical tool for determining the relative contributions of various factors to a single event or outcome. The purpose of this article is to focus on analyses where multiple predictors are considered. Such an analysis is in contrast to a univariable (or 'simple') analysis, where single predictor variables are considered. We review the basics of multivariable analyses, what assumptions underline them and how they should be interpreted and evaluated. %@ 1421-9786; 1015-9770

@article{Reboldi2013, abstract = {The term 'multivariate analysis' is often used when one is referring to a multivariable analysis. 'Multivariate', however, implies a statistical analysis with multiple outcomes. In contrast, multivariable analysis is a statistical tool for determining the relative contributions of various factors to a single event or outcome. The purpose of this article is to focus on analyses where multiple predictors are considered. Such an analysis is in contrast to a univariable (or 'simple') analysis, where single predictor variables are considered. We review the basics of multivariable analyses, what assumptions underline them and how they should be interpreted and evaluated.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Reboldi, Gianpaolo and Angeli, Fabio and Verdecchia, Paolo}, biburl = {https://www.bibsonomy.org/bibtex/21bd73ba06624b0a3e27d16eb8201b168/jepcastel}, city = {Department of Internal Medicine, University of Perugia, Perugia, Italy. paolo@unipg.it}, doi = {10.1159/000345491}, interhash = {b06c49efafa606696b86940f406039f7}, intrahash = {1bd73ba06624b0a3e27d16eb8201b168}, isbn = {1421-9786; 1015-9770}, issn = {1421-9786}, journal = {Cerebrovascular diseases (Basel, Switzerland)}, keywords = {BiomedicalResearch BiomedicalResearch:statistics&numericaldata CerebrovascularDisorders CerebrovascularDisorders:diagnosis CerebrovascularDisorders:mortality CerebrovascularDisorders:therapy Chi-SquareDistribution ConfoundingFactors(Epidemiology) DataInterpretation Humans LinearModels LogisticModels Models MultivariateAnalysis OddsRatio Prognosis ProportionalHazardsModels RiskFactors Statistical SurvivalAnalysis}, month = {1}, note = {7435<m:linebreak></m:linebreak>CI: Copyright (c) 2013; JID: 9100851; CIN: Cerebrovasc Dis. 2013;35(2):186. PMID: 23429278; 2012/08/07 [received]; 2012/10/24 [accepted]; 2013/02/21 [aheadofprint]; ppublish;<m:linebreak></m:linebreak>Anàlisi de dades; Introductori; Neurologia}, number = 2, pages = {187-93}, pmid = {23429297}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Multivariable analysis in cerebrovascular research: practical notes for the clinician.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/23429297}, volume = 35, year = 2013 }