Interaction Effects in Clinical Trials

In pharmaceutical research and development, multiple factors like age, gender, comorbidity, concomitant medication, genetic and environmental factors co-determine the efficacy of the new treatment. In statistical terms we say they interact with the treatment efficacy. It is impossible to estimate all of these factors. Instead, randomized controlled trials are used to ensure that no major inbalances exist regarding these factors, and an overall assessment is made. The limitation of this approach becomes obvious once the new medicine is applied in practice where benefits of new medicines are far less consistent than they are in the trials.1 Despite this limitation, interaction effects, are not routinely assessed in clinical trials, probably because the statistical methods for identifying and integrating them into the data have low power. Moreover, if we introduce a large number of interaction terms in a regression analysis, the power to demonstrate a statistical significance for the primary endpoint will be reduced. Nonetheless, the assessment of a small number of interaction terms in clinical research can be an important part of the evaluation of new drugs, particularly, if it can be argued that the interaction terms make clinically sense. The current chapter gives some important factors that may interact with the treatment efficacy, and proposes some guidelines for implementing an interaction assessment in the analysis of clinical trials, in order to better predict the efficacy / safety of new medicines in future clinical treatment of individual patients.