  One size fits all approaches to medicine have become a thing of the past as
the understanding of individual differences grows. The paper introduces a test
for the presence of heterogeneity in treatment effects in a clinical trial.
Heterogeneity is assessed on the basis of the predicted individual treatment
effects (PITE) framework and a permutation test is utilized to establish if
significant heterogeneity is present. We first use the novel test to show that
heterogeneity in the effects of interventions exists in the Amyotrophic Lateral
Sclerosis Clinical Trials. We then show, using two different predictive models
(linear regression model and Random Forests) that the test has adequate type I
error control. Next, we use the ALS data as the basis for simulations to
demonstrate the ability of the permutation test to find heterogeneity in
treatment effects as a function of both effect size and sample size. We find
that the proposed test has good power to detected heterogeneity in treatment
effects when the heterogeneity was due primarily to a single predictor, or when
it was spread across the predictors. The predictive model, on the other hand is
of secondary importance to detect heterogeneity. The non-parametric property of
the permutation test can be applied with any predictive method and requires no
additional assumptions to obtain PITEs.
