Multi-country randomised clinical trials (MRCTs) are common in the medical literature and their interpretation has been the subject of extensive recent discussion. In many MRCTs, an evaluation of treatment effect homogeneity across countries or regions is conducted. Subgroup analysis principles require a significant test of interaction in order to claim heterogeneity of treatment effect across subgroups, such as countries in a MRCT. As clinical trials are typically underpowered for tests of interaction, overly optimistic expectations of treatment effect homogeneity can lead researchers, regulators and other stakeholders to over-interpret apparent differences between subgroups even when heterogeneity tests are insignificant. In this paper we consider some exploratory analysis tools to address this issue. We present three measures derived using the theory of order statistics which can be used to understand the magnitude and the nature of the variation in treatment effects that can arise merely as an artefact of chance. These measures are not intended to replace a formal test of interaction, but instead provide non-inferential visual aids allowing comparison of the observed and expected differences between regions or other subgroups, and are a useful supplement to a formal test of interaction. We discuss how our methodology differs from recently published methods addressing the same issue. A case study of our approach is presented using data from the PLATO study, which was a large cardiovascular MRCT that has been the subject of controversy in the literature. An R package is available from the authors on request.
Schou, I. Manjula and Marschner, Ian C., "Methods for Exploring Treatment Effect Heterogeneity in Subgroup Analysis: An Application to Global Clinical Trials" (June 2014). COBRA Preprint Series. Working Paper 108.