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Using treatment process data to predict maintained smoking abstinence
Steffani R Bailey1, Sarah A Hammer, Susan W Bryson
1Stanford University School of Medicine, Stanford Prevention Research Center, 1070 Arastradero Road, Palo Alto, CA 94304-1334, USA. stbailey@stanford.edu
Objectives:
To identify distinct subgroups of treatment responders and nonresponders to aid in the development of tailored smoking-cessation interventions for long-term maintenance using signal detection analysis (SDA).
Methods:
The secondary analyses (n = 301) are based on data obtained in our randomized clinical trial designed to assess the efficacy of extended cognitive behavior therapy for cigarette smoking cessation. Model 1 included only pretreatment factors, demographic characteristics, and treatment assignment. Model 2 included all Model 1 variables, as well as clinical data measured during treatment.
Results:
SDA was successfully able to identify smokers with varying probabilities of maintaining abstinence from end-of-treatment to 52-week follow-up; however, the inclusion of clinical data obtained over the course of treatment in Model 2 yielded very different partitioning parameters.
Conclusions:
The findings from this study may enable researchers to target underlying factors that may interact to promote maintenance of long-term smoking behavior change.
