Abstract
INTRODUCTION: Dichotomized smoking abstinence (abstinent/smoking) is the standard outcome for clinical trials, but obscures smoking behavior change. Examining both smoking probability and smoking rate as outcomes may identify unique barriers to cessation (eg, craving, affect) among individuals who are receiving treatment but unable to quit. AIMS AND METHODS: Two-part latent growth modeling examined daily smoking probability and smoking rate during the first week of the quit attempt using self-reported cigarettes per day. Smoking trajectories and the effect of 12 weeks of varenicline (vs. placebo), craving, negative affect (NA), and positive affect (PA) on these trajectories were examined among 828 adults in a randomized smoking cessation trial (NCT01314001). RESULTS: On average, smoking probability was 46% on the target quit day and increased to 50% later into the week (ps < .01). Among participants continuing to smoke, daily smoking rates were reduced to 27% of baseline rates and remained stable throughout the week (p = .62). Varenicline use was associated with lower smoking probability (p < .001). Higher craving and NA were associated with a higher smoking probability and higher smoking rates (ps ≤ .05). Higher PA was associated with a higher smoking probability, but lower smoking rates (ps < .04). CONCLUSIONS: Modeling smoking behavior, versus dichotomized abstinence, reveals differences in predictors of treatment effects. Results suggest smoking probability increases early into the quit attempt while daily smoking rates remain stable. Varenicline increases the probability of abstinence. Smoking abstinence and lower smoking rates were both associated with lower cravings and NA. However, PA demonstrated differential relationships with abstinence and smoking rates. IMPLICATIONS: This study expands clinical outcomes beyond dichotomous smoking abstinence by conducting preliminary two-part latent growth models to evaluate treatment processes and barriers to cessation on both smoking probability and smoking rate. This approach provides a complementary understanding of treatment effects and predictors of outcomes. Results suggest time, treatment, craving, and affect may have differential relationships with smoking probability versus smoking rates which would not be captured in traditional modeling for clinical trial outcomes.