Treatment effect heterogeneity occurs when individual characteristics influence the effect of a treatment. We propose a novel approach that combines prognostic score matching and conditional inference trees to characterize effect heterogeneity of a randomized binary treatment. One key feature that distinguishes our method from alternative approaches is that it controls the Type I error rate, i.e., the probability of identifying effect heterogeneity if none exists and retains the underlying subgroups. This feature makes our technique particularly appealing in the context of clinical trials, where there may be significant costs associated with erroneously declaring that effects differ across population subgroups. TEHTrees are able to identify heterogeneous subgroups, characterize the relevant subgroups and estimate the associated treatment effects. We demonstrate the efficacy of the proposed method using a comprehensive simulation study and illustrate our method using a nutrition trial dataset to evaluate effect heterogeneity within a patient population.
翻译:当个别特征影响治疗的效果时,就会发生治疗效果的异质性。我们建议一种新颖的方法,将预测分比匹配和有条件的推断树结合起来,对随机二进制治疗的效果异质性进行定性。我们的方法与替代方法的不同一个关键特征是,它控制了I型错误率,即如果不存在的话,确定效应异质性的概率,并保留了基础子群。这个特征使我们的技术在临床试验中特别具有吸引力,因为错误地宣布影响在人口分组之间有差异可能带来巨大的成本。TEHTrees能够确定不同的分组,给相关分组定性并估计相关的治疗效果。我们用全面的模拟研究来证明拟议方法的有效性,并用营养试验数据集来评估患者群体中的效果异质性。