  Identifying patient subgroups with different treatment responses is an
important task to inform medical recommendations, guidelines, and the design of
future clinical trials. Existing approaches for subgroup analysis primarily
focus on Randomised Controlled Trials (RCTs), in which treatment assignment is
randomised. Furthermore, the patient cohort of an RCT is often constrained by
cost, and is not representative of the heterogeneity of patients likely to
receive treatment in real-world clinical practice. Therefore, when applied to
observational studies, such approaches suffer from significant statistical
biases because of the non-randomisation of treatment. Our work introduces a
novel, outcome-guided method for identifying treatment response subgroups in
observational studies. Our approach assigns each patient to a subgroup
associated with two time-to-event distributions: one under treatment and one
under control regime. It hence positions itself in between individualised and
average treatment effect estimation. The assumptions of our model result in a
simple correction of the statistical bias from treatment non-randomisation
through inverse propensity weighting. In experiments, our approach
significantly outperforms the current state-of-the-art method for
outcome-guided subgroup analysis in both randomised and observational treatment
regimes.
