Equivalence Tests in Subgroup Analyses
Confirmatory clinical trials that aim to demonstrate the efficacy of drugs are typically performed in broad patient populations so that the patient population is usually heterogeneous with respect to demographic variables and medical conditions. Therefore, regulatory guidelines request that, in addition to the primary comparison of the treatment effects in the total study population, the consistency of the treatment effect be evaluated across medically relevant subgroups (e.g. gender, age or comorbidities).
We propose that the consistency of the treatment effect in two subgroups should be assessed using an equivalence test, which in the current context we call consistency test. The proposed tests compare the treatment contrasts in the two subgroups, aiming to reject the null hypothesis of heterogeneity.
We present tests for both quantitative and binary outcome variables. While the details of these tests differ for the two types of outcome variable, both tests are based on a generalised linear model in which treatment, subgroup, and subgroup-by-treatment interaction terms are fitted.
In this text, we review the basic properties of these consistency tests using Monte-Carlo simulations. A key objective of these simulations is to suggest suitable equivalence margins, based on the performance of the tests in various settings. The investigation indicates that equivalence tests can be used both to assess the consistency of treatment effects across subgroups and to detect medically relevant heterogeneity in treatment effects across subgroups.
KeywordsLinear model Statistical interaction Subgroup analysis Binary endpoint Similarity Homogeneity Consistency
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