Improving Event Time Prediction by Learning to Partition the Event Time
  Space
Jimmy Hickey Ricardo Henao Daniel Wojdyla Michael Pencina Matthew M. Engelhard
Recently developed survival analysis methods improve upon existing approaches
by predicting the probability of event occurrence in each of a number
pre-specified (discrete) time intervals. By avoiding placing strong parametric
assumptions on the event density, this approach tends to improve prediction
performance, particularly when data are plentiful. However, in clinical
settings with limited available data, it is often preferable to judiciously
partition the event time space into a limited number of intervals well suited
to the prediction task at hand. In this work, we develop a method to learn from
data a set of cut points defining such a partition. We show that in two
simulated datasets, we are able to recover intervals that match the underlying
generative model. We then demonstrate improved prediction performance on three
real-world observational datasets, including a large, newly harmonized stroke
risk prediction dataset. Finally, we argue that our approach facilitates
clinical decision-making by suggesting time intervals that are most appropriate
for each task, in the sense that they facilitate more accurate risk prediction.