We used Cox proportional hazard regression models to analyze smoking relapse likelihood (smoking on 7 consecutive days) over the 90 day period after the quit date, the primary outcome. Secondary outcomes include: 1) smoking quantity (self-reported cigarettes smoked per day, CPD) for post-treatment weeks 1-8, analyzed with growth curve mixed models for repeated measures per subject, 2) biochemically confirmed 7-day abstinence at 8 weeks analyzed with logistic regression, and 3) continuous abstinence (complete abstinence for 90 days post quit), also analyzed with logistic regression. The primary predictor was CYP2A6 genotype-based metabolic function, which was examined for interaction with treatment (active pharmacotherapy vs. placebo). We tested whether the hazard ratio for relapse associated with treatments differed across predicted metabolic function groups by including a product interaction term in the Cox proportional hazard regression. Covariates included gender, age, and cigarettes per day (in 4 levels: ≤10, 11-20, 21-30, ≥31, while specific cigarette counts were used as the dependent variable for the smoking quantity analysis).
