  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 .
