RECENT ADVANCES IN THE ANALYSIS OF SURVIVAL DATA WITH MULTIPLE EVENTS: THE ILNESS-DEATH MODEL

In many medical studies, patients may experience several events across a follow-up period. Analysis in such studies is often performed using multi-state models. These models can be successfully used for describing complex event history data, for example, describing stages in the disease progression of a patient. The so-called “illness-death” model plays a central role in the theory and practice of these models. Many time-to-event data sets from medical studies with multiple end points can be reduced to this generic structure. In these models one important goal is the modeling of transition rates but biomedical researchers are also interested in reporting interpretable results in a simple and summarized manner. We aim to introduce feasible estimation methods for several predictive probabilities while providing some practical recommendations. Some of these methods will be used to introduce new tests for the Markov assumption which is commonly used to analyze multi-state survival data. The proposed methods are illustrated using real data. Software in the form of an R package developed by the authors will be introduced.