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Analysis of recurrent events is becoming increasingly popular for understanding treatment effects in randomised controlled trials. The analysis of recurrent events can improve efficiency and capture disease burden compared to standard time-to-first event analyses. However, the added knowledge about the multi-state process comes at the cost of modelling complexity. High mortality rates can complicate matters even more. A case study using data from a randomised controlled trial, LEADER, is presented to highlight interpretation of common methods as well as potential pitfalls when analysing recurrent events in the presence of a competing risk. The presented methods either target features of the underlying intensity functions or marginal traits of a multi-state process which includes terminal events or not. In particular, approaches to handle death as a part of an event and as a competing risk are discussed. A new method targeting the marginal mean function for a composite endpoint, which includes both death as a component and as a competing risk, will be introduced. Finally, recommendations for how to capture meaningful treatment effects in randomised controlled trials when analysing recurrent and terminal events will be made. K E Y W O R D Scompeting risks, randomised controlled trials, recurrent events, treatment effects | INTRODUCTIONThe analysis of time-to-event outcomes is well-known for quantifying treatment effects for randomised controlled trials. A common choice is to analyse the waiting times until the events of interest using a Cox regression model with treatment as a covariate, for instance analysing the time-to-first heart failure or time-to-first stroke. 1 Recurrent events are events, of the same type, that can happen several times for an individual during the course of their life, for example, hospitalisation or stroke. The analysis of recurrent events has become more popular within the last years, especially within the field of cardiology. 2 This owes to the fact that many classical time-to-event analyses of, for example, heart failure only focus on the first events, despite the recurrent event structure of the data. Using all events, and conducting recurrent event analyses, can help capture disease burden, aid understanding of treatment effects and utilise all data compared to the time-to-first event analyses. However, the analysis of recurrent events can be more complicated in

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