Statistical methods for primary and secondary outcomes {20a}
REGRESSIONS
We will conduct intent-to-treat (ITT) and contamination-adjusted intent-to-treat (CA-ITT) analyses to estimate the effects of the smoking relocation/cessation and resident endorsement interventions compared to the standard program implemented by NYCHA (no treatment). CA-ITT analyses will account for unanticipated contamination between trial arms and be completed via two-stage instrumental variables regressions using the original random allocation codes per assigned unit. Preliminary data analysis will include examination of distribution and summary statistics of all variables by intervention groups at each time point. The continuous variables with skewed distribution will be properly transformed, if necessary, to reduce impact of extreme values or improve model fitting. We will use box-plots to examine how distribution of a quantitative variable varies by categories of a categorical variable, and use scatter plots and Spearman correlation coefficient to examine bivariate associations between quantitative variables. Chi-square and Kruskal-Wallis tests will be used to detect group differences in categorical and quantitative baseline variables, respectively. For successful randomization, we expect no difference in the distribution of all baseline variables among the four arms. The baseline variables that differ by intervention groups and are related to outcomes of interest will be controlled in the aim-specific models.