Speaker
Description
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. Prognostic models for recurrent events can be utilised to predict individual patient risk of disease recurrence or outcome at certain time points.
Methods for analysing recurrent event data are not widely known or applied in research. Most analyses use survival analysis to consider time until the first event, meaning subsequent events are not analysed and key information is lost. An alternative is to analyse the event count using Poisson or Negative Binomial regression. However, this ignores the timing of events. Recurrent event methods analyse both the event count and the timing between events meaning key information is not discarded. Various methods to analyse recurrent event data exist, but evidence is lacking regarding which recurrent method is most appropriate under different scenarios.
Methods
A systematic review on methodology for analysing recurrent event data in prognostic models was conducted. Results from this review identified methods commonly used in practice to analyse recurrent event data. A simulation study was then conducted which evaluated the most frequently identified methods in the systematic review with respect to the underlying event rate. The event rates were categorised into low, medium and high based on data collected in the systematic review to best represent a variety of chronic conditions or illnesses where recurrent events are typically seen.
Results
The simulation study provided evidence to determine if model choice may be influenced by the underlying event rate in the data. This was assessed by deriving statistics suitable for recurrent event methods to assess the model fit and predictive performance of the recurrent event methods. These statistics were used to determine if certain methods identified tended to perform better than others under different scenarios.
Conclusion
Results from the systematic review and simulation study will be presented including a summary of each method identified. The results will be the first step towards a toolkit for future analysis of recurrent event data, providing evidence which recurrent event analysis method may be better suited given the data being modelled.
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