Sample sizes for treatment trials with categorical outcomes are conventionally derived by balancing three elements: a difference between alternative treatments in the event rates for the outcomes of interest (commonly termed the clinically important difference), the alpha error tolerance (false positive risk) and the beta error tolerance (false negative risk). Clinically important differences used to plan trials are chosen in part based on earlier experience with similar interventions (i.e., biological or clinical plausibility). Methodological conventions and clinicians' perceptions will also affect choices. Lastly, practical concerns about the feasibility of accruing large numbers of subjects may drive trialists to specify bigger differences as clinically important, with a view to containing sample size requirements. We suggest that patients or other members of the public be given an active role in determining the magnitude of the clinically important treatment effect for trial planning. Probability trade-offs could be constructed to enable patients and/or healthy volunteers to indicate the degree of benefit they would want from a "new" treatment, given the potential side-effects of the same treatment. This method has the advantage of respecting patient autonomy and principles of informed consent. It provides an additional consideration when plausible effect sizes and error tolerances on hypothesis tests are balanced against feasibility of accruing various sample sizes. Its primary disadvantage is inconvenience, as it adds another step to trial design. On the other hand, if patient-based clinically important differences are generated for a variety of disease states and types of treatments, specific trade-off exercises may be needed only for unusual trials. Another disadvantage is that patients' perspectives may differ markedly from clinicians' or from society at large, leading to conflicts of perspective that could prove difficult to resolve. However, we believe that raising and addressing such conflicts of perspective is itself a useful part of the evaluative process.
Patient enrolment models