Clinical Trials

Clinical Trials: A Practical Guide ■❚❙❘• the smallest treatment effect or benefit that we are trying to detect• the significance level at which we will reject the null hypothesis thatthere is no difference in the treatment effects• the power with which we want to detect an effect• the design of the study (parallel or crossover, etc.)• the expected dropout rate of subjects during the studyExampleAn RCT is planned to evaluate the effect of a new drug on reducing the deathrate, at 12 months’ follow-up, of subjects with severe coronary heart disease.Determine the expected event rate in the control armTo estimate the expected rate of death at 12 months (the mortality rate) in the controlgroup, we must review previous studies and registries of subjects with severe coronarydisease. In this example, the mortality rate at 12 months is 12%, denoted by π 1.Evaluate the smallest treatment effect that is clinically worth detectingWhen determining the size of a clinically relevant treatment effect, it helps tosystematically review the literature on previous studies and to discuss trial designwith experts. In this example, the mortality rate seen in the treatment group mightbe 10% (denoted by π 2). Therefore, the absolute treatment effect that we are tryingto detect is 2% (12%–10%), denoted by δ. At this point in the trial it is not knownwhether the new drug will be beneficial or not, but we need enough subjects enrolledin the trial to have a good chance of being able to detect such a difference. By‘detect’, we mean that if a treatment benefit is seen, it should be of such a magnitudeto be statistically significant (ie, it should have a calculated P-value