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these results presented in supplementary table 4 Sample size: Using certain assumptions and previously validated methods, it was determined that a sample size of 516 would provide 90% power to detect a 4.1 mean difference in change in KCCQ-CSS and 99% power to detect a 9.9% mean difference in change in body weight Alpha of 0.05 was split between the two primary endpoints, 0.04 for KCCQ-CSS and 0.01 for body weight Primary analysis model for the dual primary endpoints is a linear regression analysis of covariance Missing data points filled using an established and previously described 2 imputation modelthat uses baseline characteristics, prior endpoint data, and group data to produce an unfavorable value Statistical testing hierarchy Used treatment policy estimand results Confirmatory secondary endpoints were meant to represent the overall clinical benefit/harm of the intervention