The research proposed will apply recent advances in biochemical epidemiology to the development and evaluation of a state-of-the-art lung cancer prevention program. Specifically, we will test bio-behavioral interventions which incorporate personalized biomarker feedback about tobacco exposure (i.e., carbon monoxide (CO) levels) and host susceptibility to tobacco (i.e., debrisoquine metabolic phenotype) into a standard minimal contact smoking cessation intervention. Subjects will be 1,000 male and female smokers recruited through newspaper advertisements, including those targeted to minorities. Eligible smokers will complete an initial evaluation to assess baseline levels of the following variables; smoking history, perceptions of vulnerability, decisional balance ('pros' and cons"""""""" of smoking), stage and processes of smoking behavior change, coping style, and psychological adaptation. Subjects will be assigned randomly to one of three study conditions: (1) standard Quit-Smoking Consultation (QSC); (2) QSC + Exposure Biomarker feedback (EBF, or (30 QSC+EBF+Susceptibility Biomarker feedback (SBF). The QSC is a minimal contact intervention which is based on the Free and Clear Guide developed and evaluated in a large, randomized self-help quit smoking trial (Orleans et al., 1991). The QSC+EBF also includes a motivational intervention delivered prior to the QSC - i.e., provision of standardized feedback of exhaled carbon monoxide levels. The QSC+EBF+SBF includes an additional motivational intervention delivered prior to the QSC=EBF - i.e., provision of standardized feedback of testing for debrisoquine metabolic phenotype which has been associated with individual susceptibility to lung cancer. Three an 12-month follow- up assessments will be used to asses short-and long-term changes in perceived vulnerability, decisional balance, stage and processes of change, as well as adherence to recommended quitting strategies, quit rates, and psychological adaptation. Also, we will determine whether individuals biologic' (metabolic phenotype) and psychologic profiles (coping style) moderate the impact of the interventions on the above outcomes. Finally, we will use regressive models to identify key variables and causal processes associated with progress through stages of smoking behavior change and cessation.
Wileyto, E Paul; Li, Yimei; Chen, Jinbo et al. (2013) Assessing the fit of parametric cure models. Biostatistics 14:340-50 |
Bergen, Andrew W; Javitz, Harold S; Krasnow, Ruth et al. (2013) Nicotinic acetylcholine receptor variation and response to smoking cessation therapies. Pharmacogenet Genomics 23:94-103 |
Javitz, Harold S; Lerman, Caryn; Swan, Gary E (2012) Comparative dynamics of four smoking withdrawal symptom scales. Addiction 107:1501-11 |
Li, Yimei; Wileyto, E Paul; Heitjan, Daniel F (2011) Statistical analysis of daily smoking status in smoking cessation clinical trials. Addiction 106:2039-46 |
Javitz, Harold S; Swan, Gary E; Lerman, Caryn (2011) The dynamics of the urge-to-smoke following smoking cessation via pharmacotherapy. Addiction 106:1835-45 |
Li, Yimei; Wileyto, E Paul; Heitjan, Daniel F (2010) Modeling smoking cessation data with alternating states and a cure fraction using frailty models. Stat Med 29:627-38 |
Guo, Mengye; Heitjan, Daniel F (2010) Multiplicity-calibrated Bayesian hypothesis tests. Biostatistics 11:473-83 |
Bergen, Andrew W; Conti, David V; Van Den Berg, David et al. (2009) Dopamine genes and nicotine dependence in treatment-seeking and community smokers. Neuropsychopharmacology 34:2252-64 |
Heitjan, D F; Asch, D A; Ray, Riju et al. (2008) Cost-effectiveness of pharmacogenetic testing to tailor smoking-cessation treatment. Pharmacogenomics J 8:391-9 |
Heitjan, Daniel F; Guo, Mengye; Ray, Riju et al. (2008) Identification of pharmacogenetic markers in smoking cessation therapy. Am J Med Genet B Neuropsychiatr Genet 147B:712-9 |
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