Predictors
REGRESSION, SEPARATION
We also performed exploratory post hoc analyses that may help inform the design of future studies. We used logistic regression to examine the associations between baseline characteristics and smoking behavior outcomes at 52 weeks. Each independent baseline variable was evaluated as a predictor in either 1 or 2 types of models using the ITT and responder data sets. The first model evaluated the interaction of the independent baseline variable with the randomized cohort. The interaction was tested for significance compared with a model without the interaction using the joint test. If the interaction was significant, the results were reported. If not, then the second model without the interaction was applied. In this model, the independent baseline variable was adjusted for the randomization cohort, and a type-3 test was used for detection of statistical significance. One This evaluation was completed for the binary outcomes of self-reported 7-day and 30-day PPA, biovalidated 7-day PPA, self-reported continuous abstinence, and biovalidated continuous abstinence. In contrast to the analysis of outcomes, these models were not adjusted for the 4 randomization variables. Certain categorical baseline variables were collapsed either because of insufficient tallies for model convergence or to simplify the model evaluation. These baseline variables included ethnicity, education, income, health, use of noncigarette tobacco products, past quit methods used, and first cigarette smoked after waking. In addition, some of the interaction models produced quasi-complete separation, and the data were modeled again with logistic regression using a Firth bias correction [