Primary endpoint
REGRESSION, EVENTS, EVENT
The occurrence of the primary composite endpoint will be presented per treatment group as absolute numbers and percentages. The difference of the occurrence of the primary endpoint between the two treatment groups and a two-sided 95% confidence interval will be calculated. Non-inferiority will be concluded if the upper limit of a two-sided 95% confidence interval for the difference in proportion of patients at month 9 after randomisation between the two treatment arms is less than 10% points. Superiority will be concluded if the confidence interval excludes 0. As additional sensitivity analyses confidence intervals will be calculated being adjusted for study stratifications (such as centres) using Cochrane-Mantel-Haenszel weights. Also logistic regression models will be used using additional factors (such as sex) and covariate (such as age at randomisation). To explore the composite endpoint further, time-to-event endpoint considering the time from randomisation till time of first event will be analysed. We will visualise these data by presenting Kaplan-Meier curves for each treatment arm. Furthermore, Cox-regressions models will be performed adjusting for the same factors and covariates as used in the logistic regression models. To address the repetitive nature of the events used in the definition of the primary endpoint, we will also perform supportive analyses for recurrent event data. We will fit a negative binomial regression model for the events of the composite endpoints accounting for the time a patient is under risk. Furthermore, we will perform the counting process model of Andersen-Gill and frailty models. To assess the impact of the individual components of the composite endpoints, each component will be analysed descriptively.