Missing data
SNOT-22 questionnaires with up to 20% of items missing were imputed with the average of the completed questions used for missing items. A sensitivity analysis using multiple imputation was also carried out that included all participants who provided consent and were randomised, including those with missing SNOT-22 scores at the primary endpoint. Baseline variables found to be predictive of missing data status were included in multiple imputation equations. To make the missing at random assumption as plausible as possible, the multiple imputation equation included baseline data on sex, NOSE scale categories, baseline SNOT-22 score, and predictors of missing data. Overall, 1000 multiple imputation datasets were created in STATA16 using chained equations. A conservative approach was adopted, and treatment group was included in the imputation model.