Statistical Analysis
We projected a sample size of 400 veterans to detect a clinically significant difference between intervention and control groups in SBP change of 5 mm Hg between baseline and 12-month follow-up.We examined distributions of demographic and clinical variables of participants in the intervention and control arms and reported summary statistics for all baseline covariates and outcomes (means [SDs] for continuous variables; frequencies [percents] for categorical variables) by randomized group. We examined the association between baseline measures and outcome variables by randomized group for the linearity and equality of slopes necessary for use in subsequent statistical models. All analyses were performed with R, version 4.3.0 (R Foundation for Statistical Computing), and SAS, version 9.4 (SAS Institute Inc).Primary intent-to-treat analyses compared changes in SBP from baseline to 12 months between the intervention and control groups using a mixed-effects model adjusted for baseline SBP values and treatment group. We included a physician-level and peer health coach–level (for intervention group only) random effect to account for the correlation of multiple patients per physician with multiple patients per peer health coach.We performed prespecified sensitivity analyses to examine whether number of coach visits influenced the effect of the intervention on the primary outcome. We also conducted a post hoc stratified analysis before and after the start of the COVID-19 pandemic since trial activities were conducted remotely after March 2020. Participants with 12-month outcomes that were captured before March 5, 2020, were defined as the pre–COVID-19 subgroup, and the remaining participants were defined as the post–COVID-19 subgroup. The post–COVID-19 subgroup had 128 participants (64 control participants; 64 intervention participants). Because the SF-12 had 15% missing values, we analyzed patient and clinical factors that were associated with missingness. We performed multiple imputation using mice package in R to impute results to allow analysis for all participants and to reduce any bias associated with the missing data (eMethods and eTables 1 and 2 in 