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dc.contributor.authorGreenwood, CJ
dc.contributor.authorYoussef, GJ
dc.contributor.authorLetcher, P
dc.contributor.authorMacdonald, JA
dc.contributor.authorHagg, LJ
dc.contributor.authorSanson, A
dc.contributor.authorMcintosh, J
dc.contributor.authorHutchinson, DM
dc.contributor.authorToumbourou, JW
dc.contributor.authorFuller-Tyszkiewicz, M
dc.contributor.authorOlsson, CA
dc.date.accessioned2020-12-09T22:38:08Z
dc.date.available2020-12-09T22:38:08Z
dc.date.issued2020-11-20
dc.identifierpii: PONE-D-20-18792
dc.identifier.citationGreenwood, C. J., Youssef, G. J., Letcher, P., Macdonald, J. A., Hagg, L. J., Sanson, A., Mcintosh, J., Hutchinson, D. M., Toumbourou, J. W., Fuller-Tyszkiewicz, M. & Olsson, C. A. (2020). A comparison of penalised regression methods for informing the selection of predictive markers. PLOS ONE, 15 (11), https://doi.org/10.1371/journal.pone.0242730.
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/11343/253025
dc.description.abstractBACKGROUND: Penalised regression methods are a useful atheoretical approach for both developing predictive models and selecting key indicators within an often substantially larger pool of available indicators. In comparison to traditional methods, penalised regression models improve prediction in new data by shrinking the size of coefficients and retaining those with coefficients greater than zero. However, the performance and selection of indicators depends on the specific algorithm implemented. The purpose of this study was to examine the predictive performance and feature (i.e., indicator) selection capability of common penalised logistic regression methods (LASSO, adaptive LASSO, and elastic-net), compared with traditional logistic regression and forward selection methods. DESIGN: Data were drawn from the Australian Temperament Project, a multigenerational longitudinal study established in 1983. The analytic sample consisted of 1,292 (707 women) participants. A total of 102 adolescent psychosocial and contextual indicators were available to predict young adult daily smoking. FINDINGS: Penalised logistic regression methods showed small improvements in predictive performance over logistic regression and forward selection. However, no single penalised logistic regression model outperformed the others. Elastic-net models selected more indicators than either LASSO or adaptive LASSO. Additionally, more regularised models included fewer indicators, yet had comparable predictive performance. Forward selection methods dismissed many indicators identified as important in the penalised logistic regression models. CONCLUSIONS: Although overall predictive accuracy was only marginally better with penalised logistic regression methods, benefits were most clear in their capacity to select a manageable subset of indicators. Preference to competing penalised logistic regression methods may therefore be guided by feature selection capability, and thus interpretative considerations, rather than predictive performance alone.
dc.languageEnglish
dc.publisherPUBLIC LIBRARY SCIENCE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleA comparison of penalised regression methods for informing the selection of predictive markers
dc.typeJournal Article
dc.identifier.doi10.1371/journal.pone.0242730
melbourne.affiliation.departmentMelbourne School of Psychological Sciences
melbourne.affiliation.departmentPaediatrics (RCH)
melbourne.source.titlePLoS One
melbourne.source.volume15
melbourne.source.issue11
melbourne.source.pagese0242730-
melbourne.identifier.nhmrcAPP1082406
melbourne.identifier.arcDP130101459
dc.rights.licenseCC BY
melbourne.elementsid1480435
melbourne.openaccess.pmchttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC7678959
melbourne.contributor.authorOlsson, Craig
melbourne.contributor.authorLetcher, Primrose
melbourne.contributor.authorMacDonald, Jacqueline
melbourne.contributor.authorSanson, Ann
melbourne.contributor.authorHutchinson, Delyse
melbourne.contributor.authorMcIntosh, Jennifer
dc.identifier.eissn1932-6203
melbourne.identifier.fundernameidNHMRC, APP1082406
melbourne.identifier.fundernameidAustralian Research Council, DP130101459
melbourne.accessrightsOpen Access


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