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.
The Ghosh and Lin (2002) non-parametric analysis of heart failure hospitalisations takes mortality into account whilst also adjusting for different follow-up times and multiple hospitalisations per patient. Another option is to treat the incidence of cardiovascular death as an additional event in the recurrent event process and then adopt the usual analysis strategies that will be presented and discussed in this session. An alternative approach is the use of joint modelling techniques to obtain estimates of treatment effects on heart failure hospitalisation rates whilst allowing for informative censoring.
Joint modelling techniques are appropriate when analysing rates of recurrent events whilst allowing for association with a potentially informative dropout time, or when each of the outcomes is of scientific importance to the investigators and the dependence between the two processes needs to be accounted for. One approach to joint modelling is random effects models, which assume that the recurrent hospitalisations and time-to-death are conditionally independent given a latent variable. Models of this kind are intuitively appealing as they can give a tangible interpretation that an individual’s independent frailty term measures their underlying, unobserved severity of illness, which proportionately affects both their heart failure hospitalisation rate and their time-to-death (or CV death). Joint models allow distinct treatment effects to be estimated for each of the processes, whilst taking into account the association between the two.
This talk 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.