Abstract

Many chronic diseases are characterised by nonfatal recurrent events. Examples of such include asthma attacks in asthma, epileptic seizures in epilepsy and hospitalisations for worsening condition in heart failure. Analysing all of these repeat events within individuals is more representative of disease progression and more accurately estimates the effect of treatment on the true burden of disease. This talk will start by outlining the different methods that are available for analysing recurrent event data. We shall illustrate and compare various methods of analysing data on repeat hospitalisations using simulated data and data from major trials in heart failure.

An increase in heart failure hospitalisations is associated with a worsening condition and a subsequent elevated risk of cardiovascular death, meaning that subjects may die during follow-up. A comparison of heart failure hospitalisation rates, between treatment groups, can be confounded by this competing risk of death and any analyses of recurrent events must take into consideration informative censoring that may be present. I shall outline the different methods available for analysing recurrent events in the presence of dependent censoring and the relative merits of each method shall be discussed. In addition, data from multiple large scale clinical trials in cardiovascular disease shall be used to illustrate the application of these methods. Future directions for recurrent events analysis shall also be considered.