Introduction:
In clinical trials time to event endpoints like time to death, time to hospitalization or time to myocardial infarction are often or primary interest. Although multiple events might be observed per individual, only the time to the first occurring event is considered in primary analysis. One reason for this could be that guidelines recommend analyzing the data using the same...
Researchers in biomedical research often analyse data that are subject to clustering. Independence among observations are generally assumed to develop and validate risk prediction models. For survival outcomes, the Cox proportional hazards regression model is commonly used to estimate an individual’s risk at fixed time horizons. The stratified Cox proportional hazards and the shared gamma...
Accurate analysis of multiple time-to-event endpoints is a persistent challenge in clinical research, where patients may experience several recurrent non-fatal events alongside a competing fatal event. Conventional survival analysis approaches, such as time-to-first-event analyses or the Cox proportional hazards model, often neglect recurrent events or assume independence between event types,...
Prognostic Models for Recurrent Event Data
Dr Victoria Watson1,2, Prof Catrin Tudur Smith2, Dr Laura Bonnett2
1 Phastar, London, UK
2 University of Liverpool, Department of Health Data Sciences
Background / Introduction
Prognostic models predict outcome for people with an underlying medical condition. Many conditions are typified by recurrent events such as seizures in epilepsy....
Various estimators for modelling the transition probabilities in multi-state models have been proposed, e.g., the Aalen-Johansen estimator, the landmark Aalen-Johansen estimator, and a hybrid Aalen-Johansen estimator. While the Aalen-Johansen estimator is generally only consistent under the rather restrictive Markov assumption, the landmark Aalen-Johansen estimator can handle non-Markov...
