Missing data
Missing data might occur by patients’ withdrawal from trial, unreturned questionnaires after the consultation, or other circumstances. Reasons for withdrawal will be reported for each randomization group and compared qualitatively. The effect that any missing data might have on results will be assessed via sensitivity analysis of augmented data sets. Dropouts and item nonresponse will be included in the analysis by imputation methods for missing data. Missing values in the primary outcome (and in other outcomes) are replaced by multiple imputation. Through an iterative series of predictive models using the non-missing variables in the dataset, the imputation is conducted. The iterations are run until it appears that convergence has been met. The accuracy of the imputations will depend on the information density in the dataset. Diagnostic plots are used to determine how valid the imputations may be. Imputation is conducted via predictive mean matching (PMM). PMM involves selecting a datapoint from the original, non-missing data which has a predicted value close to the predicted value of the missing sample. The closest values are chosen as candidates, from which a value is chosen at random. After the imputations are completed, all of the data (complete and imputed) will be combined and the analysis performed for each imputed and completed dataset.