Identifying treatment response subgroups in observational time-to-event data

Vincent Jeanselme, Chang Ho Yoon, Fabian Falck, Brian Tom, Jessica Barrett

Keywords: Survival analysis, Treatment effect, Observational data, Subgroup discovery

Abstract: 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 rely on Randomised Controlled Trials (RCTs), in which treatment assignment is randomised. RCTs' patient cohorts are often constrained by cost, rendering them not representative of the heterogeneity of patients likely to receive treatment in real-world clinical practice. When applied to observational studies, subgroup analysis approaches suffer from significant statistical biases particularly 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.

Primary Area: causal reasoning

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Submission Number: 1922