Abstract

Decreasing costs of high-throughput ‘omics, as well as new technologies such as the Olink platform, has driven wider application in clinical trials, for example to inform precision medicine strategies. However, data-driven characterisation of patient subgroups with enhanced (or weaker) treatment effect remains a challenging problem, particularly when searching over high-dimensional biomarkers. With growing recognition that traditional approaches (e.g. exhaustive biomarker-treatment interaction testing) are sub-optimal, several promising methods have recently emerged that combine machine learning tools with concepts from causal inference. In principle, they offer greater power through a combination of less conservative multiplicity control, and the ability to capture complex multivariate signatures which may be missed during one-at-a-time testing.

I will describe three causal machine learning methods for responder subgroup detection; the “Modified covariate Lasso”1, “Causal Forests”2, and the “X-Learner”3. I will compare and assess their performance in a modest simulation study motivated by real biomarker trial datasets being generated in GSK . I will then share some (anonymised) results from on-going application of these methods to detect and predict responder subgroups from transcriptomic data measured in two Phase 3 Lupus trials. Finally, I will close with a discussion on our experience of the benefits and limitations of existing approaches in this space.