Statistical methods
SECONDARY
Patient and parent characteristics in both study arms will be evaluated using appropriate measures of central tendency and spread for continuous variables and proportions for categorical variables. For the primary MRP outcome analysis, we will assess for differences in MRP counts between the intervention and control groups at 90 days using generalized linear models with Poisson response distribution and log link function. The overall effectiveness of the intervention will be assessed by testing the model coefficient for randomization group, with a null hypothesis of no mean difference in MRP counts at 90 days between treatment and control groups. Model checking and diagnostics will be performed to assess validity of model assumptions, with appropriate remedial measures taken as necessary. For the secondary outcome analysis, we will assess for differences in outcome changes over time between the intervention and control groups using generalized linear mixed models, which accounts for correlation between repeated outcome measurements over time. Within-subjects correlation will be accounted for using a random intercept.Towards assessment of patient and parent factors modifying pMTM effectiveness, we will employ similar generalized linear mixed models. Each of the pre-specified effect modifiers will be modeled as an interaction term between the intervention variable (binary) and the effect modifier variable (binary). The test of the null hypothesis that the interaction term’s coefficient is equal to 0 will indicate whether there is evidence that the effectiveness of the intervention varies according to the proposed modifier.Towards implementation fidelity, comparisons focusing on differences between the intervention and usual care arms will be made using generalized linear mixed models with logistic link and a random intercept for provider to account for correlation within providers. Comparisons in time, focusing on whether there is a difference between arms in the time a provider spends addressing medication-related issues, will be made using linear mixed models, with a random intercept for provider to account for correlation within providers.For analysis of program implementation and replication costs, our primary measures of cost will be the average amount of time of clinic staff devoted to the pMTM intervention and usual care and the average cost per-patient for pMTM and usual care. The average time will be calculated for each category of clinic staff, including the pharmacist, by the mean time measured in the audio recordings plus the mean time reported in the annual surveys. The average cost per-patient will convert the average time measures to dollars using Bureau of Labor Statistics data and add in the cost of other clinic resources divided by the number of patients. We will also conduct a sensitivity analysis using different time measures based on the distribution of the time measures across the audio recordings and survey responses.For all qualitative data, we will employ qualitative content analysis throughout the periods of data collection and analysis [