  Estimating the effects of interventions on patient outcome is one of the key
aspects of personalized medicine. Their inference is often challenged by the
fact that the training data comprises only the outcome for the administered
treatment, and not for alternative treatments (the so-called counterfactual
outcomes). Several methods were suggested for this scenario based on
observational data, i.e.~data where the intervention was not applied randomly,
for both continuous and binary outcome variables. However, patient outcome is
often recorded in terms of time-to-event data, comprising right-censored event
times if an event does not occur within the observation period. Albeit their
enormous importance, time-to-event data is rarely used for treatment
optimization.
  We suggest an approach named BITES (Balanced Individual Treatment Effect for
Survival data), which combines a treatment-specific semi-parametric Cox loss
with a treatment-balanced deep neural network; i.e.~we regularize differences
between treated and non-treated patients using Integral Probability Metrics
(IPM). We show in simulation studies that this approach outperforms the state
of the art. Further, we demonstrate in an application to a cohort of breast
cancer patients that hormone treatment can be optimized based on six routine
parameters. We successfully validated this finding in an independent cohort.
BITES is provided as an easy-to-use python implementation.

Methodology;Machine Learning