Data Analysis
EMA
All analyses were conducted in SPSS (version 28; IBM Corp). A series of multilevel models (MLMs) were estimated to assess changes in weekly EMA compliance over time and to evaluate participant and study characteristics as predictors of compliance. Morning and random assessment compliance rates were analyzed in the same model to allow direct comparisons between assessment types. This yielded a data set with up to 18 repeated measures per participant (2 assessment types×9 weeks).First, a series of unconditional MLMs were used to establish the basic shape of trajectories of change. We started with an intercept-only model (with random intercept) to estimate the interclass correlation coefficient (ICC), then linear and quadratic slopes were added to examine the shape of change. Unconditional models were run using a maximum-likelihood estimator to compare model fit using likelihood ratio tests and to determine the structure of random effects. All other models used the default estimator (restricted maximum likelihood).Second, after determining the best fitting model to characterize change, predictors of compliance were evaluated. Assessment type was included in all predictor models. Additional predictors were mean-centered age, employment status (full-time vs not full-time, which included part-time employed, unemployed, retired, homemakers, and disabled participants), EMA phone type (study phone vs participant’s personal phone), COVID-19 context (enrolled before vs after March 22, 2020), treatment group (extended vs standard varenicline run-in), as well as additional participant demographics, including race (Black or African American vs White), sex (male vs female), income (<US $50,000 vs ≥ US $50,000 yearly), education (high school or less vs some college or greater), and study dropout (study completers vs study dropouts). Predictors were first considered in separate individual models evaluating the first-order effect of the predictor, the interaction with the slope over time, the interaction with assessment type (morning vs random assessments), and the 3-way interaction between the predictor, slope, and assessment type. After assessing predictors of compliance individually, a single model including all significant effects from the individual models assessed the unique effects of each predictor on EMA compliance. Simple slopes and effects were used to probe significant interactions [