Using decision tree analysis to identify risk factors for relapse to smoking.
This research used classification tree analysis and logistic regression models to identify risk factors related to short- and long-term abstinence. Baseline and cessation outcome data from two smoking cessation trials, conducted from 2001 to 2002 in two Midwestern urban areas, were analyzed. There were 928 participants (53.1% women, 81.8% White) with complete data. Both analyses suggest that relapse risk is produced by interactions of risk factors and that early and late cessation outcomes reflect different vulnerability factors. The results illustrate the dynamic nature of relapse risk and suggest the importance of efficient modeling of interactions in relapse prediction.
Piper
Megan E
ME
Center for Tobacco Research and Intervention, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin 53711, USA. mep@ctri.medicine.wisc.edu
Loh
Wei-Yin
WY
Smith
Stevens S
SS
Japuntich
Sandra J
SJ
Baker
Timothy B
TB
D000328:Adult; D003663:Decision Trees; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016032:Randomized Controlled Trials as Topic; D012307:Risk Factors; D055502:Secondary Prevention; D012907:Smoking; D016540:Smoking Cessation / Q000379:methods; D014029:Tobacco Use Disorder; D016896:Treatment Outcome
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