Statistical Analysis
atrial fibrillation, Diabetes
ATRIAL FIBRILLATION, TYPE 2 DIABETES, DIABETES
Statistical analyses of efficacy data were conducted using SAS 9.4 (SAS Institute, Cary, NC) at the Diabetes Trials Unit (Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, UK) after a prespecified statistical analysis plan (The primary end point analysis was performed on the per protocol set (PPS) of patients with valid PCr/ATP measurements available at baseline and week 12. The formal analysis employed an analysis of variance model for the primary end point hypothesis testing. Response (ie, outcome) was defined as the change (PCr/ATP absolute change) from baseline to week 12 calculated for each patient by subtracting the baseline PCr/ATP measurement from the 12-week measurement and was adjusted for treatment (empagliflozin versus placebo), history of type 2 diabetes (yes or no), and history of atrial fibrillation (yes or no). A sensitivity analysis of the primary end point was introduced that included baseline PCr/ATP value as a covariate in an analysis of covariance model. Furthermore, the same ANCOVA model was employed on the randomized set (in line with the intention-to-treat principle) of patients to check for an unbiased estimation of the primary end point result.Subgroup analyses were performed to assess the homogeneity of treatment effects on changes in the PCr/ATP ratio in different subgroups (eGFR [<60 versus >60 mL/(min1.73 mThe analysis of exploratory end points was performed with available data using descriptive statistics or ANCOVA with no adjustment for multiple testing. All summaries were produced for both cohorts using all available data.For analysis of the metabolomic data, a Wilcoxon rank-sum test was applied. The metabolites were then sorted by the signed significance of each respective test. Signed significance is defined as the negative of the logarithm (base 10) of the test probability value and multiplied by the sign of the median (the difference in medians between the 2 samples). A conventional principal component analysis (ie, using singular value decomposition) was computed by the 