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Treatment effect heterogeneity occurs when individual characteristicsinfluence the effect of a treatment. We propose a novel approach that combinesprognostic score matching and conditional inference trees to characterizeeffect heterogeneity of a randomized binary treatment. One key feature thatdistinguishes our method from alternative approaches is that it controls theType I error rate, i.e., the probability of identifying effect heterogeneity ifnone exists and retains the underlying subgroups. This feature makes ourtechnique particularly appealing in the context of clinical trials, where theremay be significant costs associated with erroneously declaring that effectsdiffer across population subgroups. TEHTrees are able to identify heterogeneoussubgroups, characterize the relevant subgroups and estimate the associatedtreatment effects. We demonstrate the efficacy of the proposed method using acomprehensive simulation study and illustrate our method using a nutritiontrial dataset to evaluate effect heterogeneity within a patient population.

Assessing effect heterogeneity of a randomized treatment using conditional inference trees