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A comparison of penalised regression methods for informing the selection of predictive markers

Greenwood, Christopher, Youssef, George, Letcher, P, Macdonald, Jacqueline, Hagg, Lauryn J, Sanson, A, Mcintosh, J, Hutchinson, Delyse, Toumbourou, John, Fuller-Tyszkiewicz, Matthew and Olsson, Craig 2020, A comparison of penalised regression methods for informing the selection of predictive markers, PLoS ONE, vol. 15, no. 11, pp. 1-14, doi: 10.1371/journal.pone.0242730.

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Title A comparison of penalised regression methods for informing the selection of predictive markers
Author(s) Greenwood, Christopher
Youssef, GeorgeORCID iD for Youssef, George orcid.org/0000-0002-6178-4895
Letcher, P
Macdonald, JacquelineORCID iD for Macdonald, Jacqueline orcid.org/0000-0001-9451-2709
Hagg, Lauryn JORCID iD for Hagg, Lauryn J orcid.org/0000-0001-7305-4980
Sanson, A
Mcintosh, JORCID iD for Mcintosh, J orcid.org/0000-0003-4709-5003
Hutchinson, DelyseORCID iD for Hutchinson, Delyse orcid.org/0000-0003-3221-7143
Toumbourou, JohnORCID iD for Toumbourou, John orcid.org/0000-0002-8431-3762
Fuller-Tyszkiewicz, MatthewORCID iD for Fuller-Tyszkiewicz, Matthew orcid.org/0000-0003-1145-6057
Olsson, CraigORCID iD for Olsson, Craig orcid.org/0000-0002-5927-2014
Journal name PLoS ONE
Volume number 15
Issue number 11
Article ID e0242730
Start page 1
End page 14
Total pages 14
Publisher Public Library of Science (PLoS)
Place of publication San Francisco, CA
Publication date 2020-11-20
ISSN 1932-6203
1932-6203
Keyword(s) Forecasting
Personality tests
Adolescents
Cohort studies
Psychometrics
Questionnaires
Longitudinal studies
Young adults
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
VARIABLE SELECTION
REGULARIZATION
DELINQUENCY
CHILDHOOD
INVENTORY
ANXIETY
Summary Background: 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.
Language eng
DOI 10.1371/journal.pone.0242730
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2020, Greenwood et al.
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30145631

Document type: Journal Article
Collections: Faculty of Health
School of Psychology
Open Access Collection
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.