Statistical Analyses
REGRESSION
We first compared patient demographics and clinical characteristics between the control and intervention groups (both combined and individually), using counts and percentages for categorical variables and means and standard deviations (SD) for quantitative variables. We used standardized mean differences (SMD)The primary analyses compared the odds of a missed appointment between the control and the combined intervention groups using appointment-level logistic regression models with non-nested clustering for repeated appointments among providers and patientsWe conducted two sensitivity analyses. To address the possibility of diminished effect after repeated exposure to the intervention, the first sensitivity analysis was restricted to each patient’s first appointment (separately for primary care and mental health). To provide a more accurate estimate of the standard errors, we used logistic generalized estimating equation models with clustering for providers using an exchangeable covariance structure.