  Non-significant randomized control trials can hide subgroups of good
responders to experimental drugs, thus hindering subsequent development.
Identifying such heterogeneous treatment effects is key for precision medicine
and many post-hoc analysis methods have been developed for that purpose. While
several benchmarks have been carried out to identify the strengths and
weaknesses of these methods, notably for binary and continuous endpoints,
similar systematic empirical evaluation of subgroup analysis for time-to-event
endpoints are lacking. This work aims to fill this gap by evaluating several
subgroup analysis algorithms in the context of time-to-event outcomes, by means
of three different research questions: Is there heterogeneity? What are the
biomarkers responsible for such heterogeneity? Who are the good responders to
treatment? In this context, we propose a new synthetic and semi-synthetic data
generation process that allows one to explore a wide range of heterogeneity
scenarios with precise control on the level of heterogeneity. We provide an
open source Python package, available on Github, containing our generation
process and our comprehensive benchmark framework. We hope this package will be
useful to the research community for future investigations of heterogeneity of
treatment effects and subgroup analysis methods benchmarking.
