Assessing consistency in clinical trials with two subgroups and binary endpoints: A new test within the logistic regression model.

Abstract

In late stage drug development, the experimental drug is tested in a diverse study population within the relevant indication. In order to receive marketing authorization, robust evidence for the therapeutic efficacy is crucial requiring investigation of treatment effects in well-defined subgroups. Conventionally, consistency analyses in subgroups have been performed by means of interaction tests. However, the interaction test can only reject the null hypothesis of equivalence and not confirm consistency. Simulation studies suggest that the interaction test has low power but can also be oversensitive depending on sample size-leading in combination with the actually ill-posed null hypothesis to findings regardless of clinical relevance. In order to overcome these disadvantages in the setup of binary endpoints, we propose to use a consistency test based on the interval inclusion principle, which is able to reject heterogeneity and confirm consistency of subgroup-specific treatment effects while controlling the type I error. This homogeneity test is based upon the deviation between overall treatment effect and subgroup-specific effects on the odds ratio scale and is compared with an equivalence test based on the ratio of both subgroup-specific effects. Performance of these consistency tests is assessed in a simulation study. In addition, the consistency tests are outlined for the relative risk regression. The proposed homogeneity test reaches sufficient power in realistic scenarios with small interactions. As expected, power decreases for unbalanced subgroups, lower sample sizes, and narrower margins. Severe interactions are covered by the null hypothesis and are more likely to be rejected the stronger they are.