Analysis
REGRESSION
Within an ITT framework, we used an available case analysis to identify the average causal effect of being assigned to watch Plan A. Equivalence was assessed using the standardized mean difference of treatment and control groups at baseline for a set of predetermined variables (age, race, ethnicity, education, sexual initiation, and the baseline measure of the outcome). For analytic and interpretive simplicity, we initially prespecified the use of ordinary least squares regression to estimate the average treatment effect for all three confirmatory outcomes (see Analysis Plan registered on clinicaltrials.gov: NCT03238313). However, because some results are sensitive to model choice, we have elected to present results in terms of the negative binomial and logistic regression models that more closely meet the distributional characteristics of the data (Hilbe, Although a difference of means should produce an unbiased estimate of the effect of Plan A on outcomes, we included the following covariates in the regression equations to improve the efficiency of the estimates: age, black race, Hispanic ethnicity, high school education, and the baseline measure of the outcome variable being estimated. We also included imputation indicators and regional dummy variables to account for blocking. All covariates were mean-centered. All analyses were conducted in Stata 16.1. We conducted sensitivity analyses (Supplementary Material), otherwise employing the benchmark approach, to test whether findings are sensitive to data management and analytic decisions.