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One size fits all approaches to medicine have become a thing of the past asthe understanding of individual differences grows. The paper introduces a testfor the presence of heterogeneity in treatment effects in a clinical trial.Heterogeneity is assessed on the basis of the predicted individual treatmenteffects (PITE) framework and a permutation test is utilized to establish ifsignificant heterogeneity is present. We first use the novel test to show thatheterogeneity in the effects of interventions exists in the Amyotrophic LateralSclerosis Clinical Trials. We then show, using two different predictive models(linear regression model and Random Forests) that the test has adequate type Ierror control. Next, we use the ALS data as the basis for simulations todemonstrate the ability of the permutation test to find heterogeneity intreatment effects as a function of both effect size and sample size. We findthat the proposed test has good power to detected heterogeneity in treatmenteffects when the heterogeneity was due primarily to a single predictor, or whenit was spread across the predictors. The predictive model, on the other hand isof secondary importance to detect heterogeneity. The non-parametric property ofthe permutation test can be applied with any predictive method and requires noadditional assumptions to obtain PITEs.

A Permutation Test for Assessing the Presence of Individual Differences in Treatment Effects