In practice the benefit of cardiovascular medicines is less consistent than it is in clinical trials. This is due to multiple uncontrolled factors that co-determine the efficacy of the new treatment. In statistical terms, they interact with the new treatment. Interaction effects are rarely assessed in cardiovascular trials. To review (1) important factors that may interact with the treatment efficacy, (2) how to examine such factors, and (3) why so. Important factors include (a) possible risk factors such as specific patient characteristics, and concomitant medications, and (b) study-specific aspects such as heterogeneities of investigators, health centres, and individual patients including patient compliance. Such factors can be assessed by comparing subgroups. A common but incorrect approach is the comparison of the significances of difference between treatment modalities in either subgroup. Instead, a direct comparison of effect sizes relative to the standard errors is adequate. As an alternative, regression modelling is adequate and convenient. Results of interaction assessments are post-hoc and, therefore, of an exploratory and unconfirmed nature. So, why should they be performed? In cardiovascular research the effects of patient characteristics and drug-drug interactions on drug efficacies are numerous. It is valuable to account at least post-hoc for such mechanisms. Second, current cardiovascular trials involve heterogeneous health centres, investigators, and patient groups. Accounting for these heterogeneities can be helpful to better predict individual responses in future patients. Cardiovascular trials enrolling patient groups at risk for heterogeneity should include at least a post-hoc assessment for interaction. Correct and incorrect methods for that purpose are described. Interaction assessments are helpful to better predict the efficacy/safety of new cardiovascular medicines in the future treatment of subgroups of patients. (Neth Heart J 2007;15:61-6.).