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A comparison of penalised regression methods for informing the selection of predictive markers
journal contribution
posted on 2020-11-20, 00:00 authored by Christopher GreenwoodChristopher Greenwood, George YoussefGeorge Youssef, Primrose LetcherPrimrose Letcher, Jacqui MacdonaldJacqui Macdonald, Lauryn J Hagg, Ann Sanson, Jenn Mcintosh, Delyse HutchinsonDelyse Hutchinson, John ToumbourouJohn Toumbourou, Matthew Fuller-TyszkiewiczMatthew Fuller-Tyszkiewicz, Craig OlssonCraig OlssonBackground: 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.
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.
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Journal
PLoS ONEVolume
15Issue
11Article number
e0242730Pagination
1 - 14Publisher
Public Library of Science (PLoS)Location
San Francisco, CAPublisher DOI
Link to full text
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1932-6203eISSN
1932-6203Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2020, Greenwood et al.Usage metrics
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