Statistical analysis
Our primary research question was whether the intervention improved health outcomes. Because there were multiple outcomes of interest, we evaluated all 13 of them independently and created a primary outcome from those analyses. The primary outcome was the number of statistically significant intervention effects (defined as To evaluate the intervention’s effect on each of the 13 outcomes, we fit linear mixed effects model for the continuous outcomes and a logistic mixed effects model for each the binary outcomes. Because the intervention was assigned at the clinic level, we account for that with a random clinic effect; this is standard practice for cluster randomized trials. So, each mixed-effects model included the baseline clinic as a random effect and the intervention as a fixed effect. Because of computational difficulties and the prior assumption that there is clinic-to-clinic variation, we used a penalized likelihood approach for the clinic variance; this is equivalent to estimating via posterior mode with a weakly informative prior [We estimated we had sufficient power of 0.80 to detect a small effect size of 0.21 overall omnibus test between the AC and SC conditions by 24 months. We assumed an intraclass area correlation of 0.01 and assumed 80% retention for the power analysis.The study [