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Exploration of treatment effect heterogeneity (TEH) is an increasinglyimportant aspect of modern statistical analysis for stratified medicine inrandomised controlled trials (RCTs) as we start to gather more information ontrial participants and wish to maximise the opportunities for learning fromdata. However, the analyst should refrain from including a large number ofvariables in a treatment interaction discovery stage. Because doing so cansignificantly dilute the power to detect any true outcome-predictiveinteractions between treatments and covariates. Current guidance is limited andmainly relies on the use of unsupervised learning methods, such as hierarchicalclustering or principal components analysis, to reduce the dimension of thevariable space prior to interaction tests. In this article we show thatoutcome-driven dimension reduction, i.e. supervised variable selection, canmaintain power without inflating the type-I error or false-positive rate. Weprovide theoretical and applied results to support our approach. The appliedresults are obtained from illustrating our framework on the dataset from an RCTin severe malaria. We also pay particular attention to the internal risk modelapproach for TEH discovery, which we show is a particular case of our methodand we point to improvements over current implementation.

Supervised variable selection in randomised controlled trials prior to exploration of treatment effect heterogeneity: an example from severe malaria