Clinical Trials

Clinical Trials: A Practical Guide ■❚❙❘an adequate washout interval (where the subject is free of drugs), the effect ofeither treatment can be influenced by whether it is administered first or second,particularly if patients can sense any real therapeutic effects. For example, in acrossover trial testing two antihypertensive drugs, both drugs are more effective inthe second period than in the first.If this period effect is large, it can be minimized by randomly allocating equalnumbers of subjects to different sequences and applying some form of statisticaladjustment. In a bioequivalence trial, such an interaction might be present even inthe absence of any carry-over (long term residual) effect. The problem is that wecannot tell whether the interaction is due to carry-over or a period-by-treatmentinteraction, as these two effects are confounded and can never be separated.Therefore, the problem of carry-over is best avoided by ensuring that a crossovertrial is properly designed (see Chapter 10).ConclusionAn interaction effect in a clinical trial is where there is a change in the magnitudeor direction of the association between a treatment and an outcome according tothe level of a third variable. Unlike a confounding effect which can be controlledduring a clinical trial, an interaction effect is an unexpected inherent modificationof the treatment effect, and can only be explored and assessed once the data havebeen collected.The identification of interaction effects can assist in targeting specific therapiesat subgroup populations who are more likely to benefit from the therapy.Interaction effects can also help treatment mechanisms to be understood, furtheringresearch in the early stages of drug development. In the later phases of drugresearch, interaction effects might affect the labeling and prescribing of the product.In summary, it is important to identify and understand interaction effects, andspecific statistical methods might be required to elucidate and quantify these effects.Finally, it is important to note that interaction tests should be used cautiously indata analysis, as most trials are not powered to detect such interaction effects,and the results of such tests are always exploratory in nature [7].References1. Gail M, Simon R. Testing for qualitative interactions between treatment effects and patientsubsets. Biometrics 1985;41:361–72.315