Abstract
Accurate models of patient survival probabilities
provide important information to clinicians prescribing care for life-threatening and terminal ailments. A recently developed class of models –
known as individual survival distributions (ISDs)
– produces patient-specific survival functions that
offer greater descriptive power of patient outcomes than was previously possible. Unfortunately, at the time of writing, ISD models almost
universally lack uncertainty quantification. In this
paper we demonstrate that an existing method for
estimating simultaneous prediction intervals from
samples can easily be adapted for patient-specific
survival curve analysis and yields accurate results.
Furthermore, we introduce both a modification
to the existing method and a novel method for
estimating simultaneous prediction intervals and
show that they offer competitive performance. It
is worth emphasizing that these methods are not
limited to survival analysis and can be applied
in any context in which sampling the distribution of interest is tractable. Code is available at
https://github.com/ssokota/spie