Using Elastic Net Penalized Cox Proportional Hazards Regression to Identify Predictors of Imminent Smoking Lapse.

Journal: Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco

Published Date: Jan 4, 2019

Abstract

INTRODUCTION: Machine learning algorithms such as elastic net regression and backward selection provide a unique and powerful approach to model building given a set of psychosocial predictors of smoking lapse measured repeatedly via ecological momentary assessment (EMA). Understanding these predictors may aid in developing interventions for smoking lapse prevention.

Authors

Robert Suchting

Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, Houston, TX.
Emily T Hébert

Oklahoma Tobacco Research Center, Stephenson Cancer Center, Oklahoma City, OK.
Ping Ma

Department of Statistics, University of Georgia, Athens, GA 30602, USA.
Darla E Kendzor

Oklahoma Tobacco Research Center, Stephenson Cancer Center, Oklahoma City, OK.
Michael S Businelle

Oklahoma Tobacco Research Center, Stephenson Cancer Center, Oklahoma City, OK.

Keywords

Adult Ecological Momentary Assessment Female Follow-Up Studies Humans Machine Learning Male Middle Aged Predictive Value of Tests Proportional Hazards Models Recurrence Smartphone Smokers Smoking Cessation Tobacco Smoking

External Resources

View on PubMed Access via DOI PubMed (29059349)

Using Elastic Net Penalized Cox Proportional Hazards Regression to Identify Predictors of Imminent Smoking Lapse.

Abstract

Authors

Keywords

External Resources

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