Specific aim 2 analyses
cancer type
The analysis for aim 2, which is restricted to only intervention encounters, is updated only to include the same patient factors included in aim 1. For the evaluation of the implementation bundle, we will summarize patient characteristics by intervention status (baseline and intervention). We will also summarize all survey responses by cluster implementation status, including clinician characteristics (age, sex, and years of practice). As with aim 1, analyses will be based on intention to treat at the patient level and at the cluster level.We will assess the effect of process measures on SPADE scores and other clinical outcomes. To assess the effect of clinician experience on implementation processes, we will assess the relationship between clinician scores and implementation outcomes by modeling each outcome as dependent on clinician scores. For count data, we will use appropriate models and select standard or zero-inflated Poisson or negative binomial models according to the Akaike information criterion. For binary measures, we will use logit models. For each outcome, we will estimate bivariate models, including only one survey score, a model with all scores, and a final model that also includes clinician characteristics. These models will include the patient factors included in model (1): cancer type and stage, as well as 7 variables selected as likely to impact the use of the portal and completion of the surveys: age, sex, and severity of illness (Elixhauser), and, according to a 9-digit zip code, area estimates of educational level, income, and broadband access.We will use the measures of adoption, fidelity, and penetration (described above) to assess the differential impact of E2C2 components on patient and clinician outcomes. Formally, we will undertake a mediation analysis with structural equation modeling to assess the extent to which each component of E2C2 mediates the intervention. We will use models appropriate for count data, selecting standard or zero-inflated Poisson or negative binomial models according to Aikake’s Information Criteria, or logit models for binary measures. For each outcome, we will estimate (a) bivariate models including only one survey score, (b) a model with all scores, and (c) a final model which also includes clinician characteristics.