Data analysis
UTIs
SECONDARY, COMPLICATION, COMPLICATIONS
Data analysis followed the intention-to-treat principle on the full analysis set, which is as close as possible to an ideal intention-to-treat population. All randomly assigned practices remained in the allocated arm for analysis. Practices for which prescription data at baseline and from Q4 were available, formed the full analysis set. Prescription data were available and analysed as aggregated data for each practice and each quarter from Qb to Q4. Depending on the data type, descriptive statistics were used to summarise central tendencies (eg, means; variability, such as standard deviations; and frequencies of participant characteristics, prescribing patterns, and complications). For the primary and secondary hypotheses, analysis of covariance (ANCOVA) was used to adjust for the baseline proportion of prescribing and for region as defined a priori. Assumptions were examined before conducting ANCOVA to ensure the appropriateness of the statistical method.Primary and secondary outcomes were treated as a continuous variable because the assumptions were met and we had aggregated data only. Complication rates were analysed as a nominal variable.Like the exploratory outcomes, the secondary outcome was considered exploratory. Thus, no adjustment for multiple testing was made. Two sided tests such as t-test or χThe proportions of two practices in Qb and one practice in Q4 that had no prescribed antibiotics or any treatment in the intervention group were set to zero to keep them in the full analysis set for the primary and secondary analyses. In sensitivity analyses, primary and secondary outcomes were assessed after excluding these three practices. Further sensitivity analyses were conducted to evaluate the robustness of the means of the primary analysis including: (univariate) weighting by the inverse variance method; excluding practices with smaller numbers of diagnoses of UTIs (less than 5, 15, or 25); adjusting for region as well as for number of patients in a mixed effects meta-regression and applying multiple imputation that replaced missing data for prescription proportions in Qb or Q4. For the multiple imputation, missing data were assumed to be missing completely at random. A thousand iterations using predictive mean matching were done and effect estimates were pooled. Missing data in Qb, Q4, age, and years in practice were imputed by using information on these variables (if available) in addition to physician’s gender, practice location (region, community), average number of patients per quarter, and whether the practice was run by a GP with or without any partners. The data analyses were performed using R version 4.0.2.