Economic analysis
REGRESSION
Multiple imputations were applied to missing primary outcome data (change in body weight, and quality of life at 52 weeks) assuming missing at random. Patterns in missing data were explored with the use of logistic regression and t-tests to investigate if any covariates would predict if an outcome variable was missing (logistic regression), and whether there were differences in patient characteristics between patient groups who had a missing outcome variable, and patient groups with a non-missing outcome variable (t-tests). To address the negatively skewed distribution for the estimated utilities, univariable generalized linear regression modelling with a Poisson distribution and log link was used to explore differences in participant QALYs following the modified Park test [Nonparametric bootstrapping with 2,000 simulations was performed around each ICER to address uncertainty around the cost-effectiveness of RUFIT-NZ. Additionally, scenario analyses exploring the cost-effectiveness of RUFIT-NZ were performed for participants with non-missing outcomes data without imputation. The cost of providing RUFIT-NZ across the three rugby clubs was estimated through micro costing (see 