Statistical methods for primary and secondary outcomes {20a}
REGRESSION, SECONDARY
The primary objective of this study is to compare mPATH™-CRC Reach (proportion of participants who complete the mPATH™-CRC program or have risk factors identified by mPATH™-CheckIn) in the 6th month following implementation between the “high touch” and “low touch” strategies. This objective will be evaluated using a mixed effects logistic regression model with mPATH™-CRC Reach (yes vs. no) as the patient-level outcome, implementation approach (“high touch” vs. “low touch”) as the primary independent variable, site as a random effect, and the stratification factor clinic size (small vs. large) as a covariate per the design. A descriptive approach will also be utilized where mPATH™-CRC Reach is estimated and reported along with an exact 95% confidence interval for each month (for months 1–6 following implementation), and within socioeconomic strata (defined by the ADI and insurance status) for “high touch” and “low touch” clinics separately. This same basic approach will be used to compare other binary implementation outcomes (mPATH™-CRC Implementation Fidelity, mPATH™-CRC Maintenance, mPATH™-CheckIn Reach, and mPATH™-CheckIn Maintenance) between the “high touch” and “low touch” strategies. Continuous implementation outcomes (mPATH™-CRC Adoption, mPATH™-CRC Acceptability, mPATH™-CheckIn Adoption, mPATH™-CheckIn Acceptability) are measured at the provider/staff level, rather than the patient level. Linear mixed effects models will be used to compare these outcomes between the “high touch” and “low touch” strategies, with the implementation approach (“high touch” vs. “low touch”) as the primary independent variable, site as a random effect, and the stratification factor clinic size (small vs. large) as a covariate per the design.The key secondary outcome of the effectiveness of mPATH™-CRC will be evaluated by comparing the proportion who complete CRC screening within 16 weeks of their visit to the clinic between a pre-implementation cohort (months 12–4 before implementation) and a post-implementation cohort (months 1–8 after implementation). Similar to the model described for mPATH™-CRC Reach above, the effectiveness of mPATH™-CRC will be evaluated using a mixed effects logistic regression model. For an overall assessment of the effectiveness, completing CRC screening within 16 weeks (yes vs. no) will be the patient-level outcome, the implementation cohort (pre vs. post) will be the primary independent variable, the site will be a random effect, and the stratification factor clinic size (small vs. large) will be a covariate per the design. Pre- vs post-implementation changes will also be compared between the “high touch” and “low touch” implementation strategies and between dose levels defined based on mPATH™-CRC usage by incorporating interactions into the mixed effects logistic regression models. In addition to the mixed model approach, an interrupted time series analysis (segmented regression) [