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dc.contributor.authorHosseinnataj, A
dc.contributor.authorBahrampour, A
dc.contributor.authorBaneshi, MR
dc.contributor.authorZolala, F
dc.contributor.authorNikbakht, R
dc.contributor.authorTorabi, M
dc.contributor.authorAbadi, FMS
dc.date.accessioned2021-05-28T00:54:53Z
dc.date.available2021-05-28T00:54:53Z
dc.date.issued2019
dc.identifier.issn1023-9510en_US
dc.identifier.doi10.22062/JKMU.2019.89573en_US
dc.identifier.urihttp://hdl.handle.net/10072/396331
dc.description.abstractBackground: Two main issues that challenge model building are number of Events Per Variable and multi collinearity among exploratory variables. Our aim is to review statistical methods that tackle these issues with emphasize on penalized Lasso regression model. The present study aimed to explain problems of traditional regressions due to small sample size and multi-colinearity in trauma and influenza data and to introduce Lasso regression as the most modern shrinkage method. Methods: Two data sets, corresponded to Events Per Variable of 1.5 and 3.4, were used. The outcomes of these two data sets were hospitalization due to trauma and hospitalization of patients suffering influenza respectively. In total, four models were developed: Classic Cox and logistic regression models, as well as their penalized lasso form. The tuning parameters were selected through 10-fold cross validation. Results: Traditional Cox model was not able to detect significance of any of variables. Lasso Cox model revealed significance of respiratory rate, focused assessment with sonography in trauma, difference between blood sugar on admission and 3 h after admission, and international normalized ratio. In the second data set, while lasso logistic selected four variables as being significant, classic logistic was able to identify only the importance of one variable. Conclusion: The AIC for lasso models was lower than that for traditional regression models. Lasso method has practical appeal when Events Per Variable is low and multi collinearity exists in the data.en_US
dc.description.peerreviewedYesen_US
dc.relation.ispartofpagefrom440en_US
dc.relation.ispartofpageto449en_US
dc.relation.ispartofissue6en_US
dc.relation.ispartofjournalJournal of Kerman University of Medical Sciencesen_US
dc.relation.ispartofvolume26en_US
dc.titlePenalized lasso methods in health data: Application to trauma and influenza data of Kermanen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dcterms.bibliographicCitationHosseinnataj, A; Bahrampour, A; Baneshi, MR; Zolala, F; Nikbakht, R; Torabi, M; Abadi, FMS, Penalized lasso methods in health data: Application to trauma and influenza data of Kerman, Journal of Kerman University of Medical Sciences, 2019, 26 (6), pp. 440-449en_US
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/en_US
dc.date.updated2020-08-07T04:36:49Z
dc.description.versionVersion of Record (VoR)en_US
gro.rights.copyright© The Author(s) 2019. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
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gro.griffith.authorBahrampour, Abbas


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