The primary goal of our parent study, the Adolescent Suicide Prevention in Routine clinical Encounters (ASPIRE) trial, is to compare the effectiveness of an electronic health record (EHR) nudge alone to an EHR nudge plus facilitation with respect to delivery of a secure firearm storage program during pediatric well-child visits. We captured clinician-reported reach in the EHR for the entire patient population (~45,000 visits) and obtained fidelity (i.e., parent-reported receipt of the program) from a subset of parents with whom we engaged in an intensive sampling approach to collect a post-visit survey. Reach was documented by clinicians for a large sample of patients and therefore does not suffer from the potential non-response bias that may affect parent-reported fidelity. However, reach may suffer from measurement error if clinicians incorrectly reported program delivery or exited out of the EHR prompt without responding to the questions. The proposed aims of this supplement will develop statistical methods to combine information contained in each outcome (reach, fidelity), effectively mitigating their respective limitations. The proposed methods will offer a powerful statistical analysis tool for comparing the effectiveness of the implementation strategies using multiple sources of data from a cluster randomized trial like ASPIRE. In Aim 1, we will derive a statistical estimator for comparing interventions in a two-arm cluster randomized trial to addresses non-response bias in survey-based fidelity by leveraging the large, complete sample provided by EHR-based reach, the latter of which may be subject to measurement error. We will also propose methods to obtain p-values and confidence intervals for the comparison of study arms. The methods will account for correlation due to inherent clustering of outcomes (patients within providers within clinics) while controlling Type I error and maximizing power to detect a difference between study arms. In Aim 2, we will use simulation studies to assess the statistical properties of the proposed estimator and inferential methods (i.e., p-values, confidence intervals). We will study bias, variability, Type I error, power, and coverage of 95% confidence intervals using simulations across a range of design factors such as the level of parent non-response in fidelity, amount of measurement error in reach, magnitude of the treatment effect, and intra-cluster correlation. In Aim 3, we will apply the proposed methods to data from ASPIRE. We will apply our methodology to jointly analyze parent-reported fidelity and EHR-based reach from the active implementation period of the ASPIRE trial. The methodology developed within our supplemental work will provide increased accuracy and precision in analyses of important implementation outcomes collected as part of the ASPIRE trial and will offer a new analytic framework for future cluster randomized trials in implementation science.