Statistical analysis
REGRESSION, DISEASE, TYPE 2 DIABETES
To assess the first prerequisite (treatment heterogeneity, defined as larger variability of HbAThe key parameter of interest in this meta-regression model is the regression coefficient for the treatment effect. If this value is 0, then there is no difference between the verum and placebo arms with respect to their log(SD) values, although there was proper adjustment for the size of the treatment effect (via the log(mean)), the correlations within trials (via the random intercept) and the different sample sizes in trial arms (via inverse-variance weighting). Values above 0 indicate a larger variability in the verum arms, i.e. treatment heterogeneity, and thus a potential for precision treatment in individuals with type 2 diabetes.To assess the second prerequisite (identification of predictors to explain treatment heterogeneity), we used a separate meta-regression model for each individual predictor. To this end, the meta-regression model as described in the previous paragraph was extended by an additional interaction term of the respective predictor with treatment. Evaluated predictors were mean age, proportion of male participants, mean BMI, mean known disease duration and the mean HbASAS, Version 9.4 (SAS Institute, Cary, NC, USA), was used for data management and analysis. As the study does not include personalised data, we did not seek a vote from an ethics committee. The study was not preregistered and had no previously published protocol.