Computing individual and collective ethical utility for optimally planning phase III trials

A quantitative evaluation of individual and collective ethics is proposed here, with the aim of providing a tool for sample size determination/estimation that goes further than the standard power setting of 80–90%. Individual ethics deal with issues that concern the patients enrolled in the trial, where collective ones concern the patients not enrolled in the trial, and who might benefit from a positive result. The global ethical utility (GEU) of a phase III trial is introduced here, being the summation of individual and collective ethical utilities, and can be viewed as a function of the sample size. The GEU model is based on the extent of the efficacy of the treatments in study, of the quality of life of the patients being treated, of the effects of potential adverse reactions, it accounts for the duration of the periods of interest and for the size of population groups, and also embeds the experimental power. This work aims at arguing the case for GEU adoption for sample size determination. The sample size that maximizes GEU can be adopted for planning the trial, even when providing a power value out of the classical range [.8,.9]. Alternatively, among the sample sizes based on power values of 80% and 90%, the one providing the highest GEU can be adopted. Intuitively, when a treatment is assumed to work well, to have few adverse effects, and is expected to improve the QoL of the ill population for a considerable amount of time, collective ethics may prevail giving ethically optimal sample sizes larger than usual, and consequent quite high power values (e.g. 99%). Instead, medium, though still clinically meaningful, levels of effect, considerable adverse reactions, and limited life expectation and QoL improvement, might shift the ethical balance on individual ethics and give an ethically optimal sample size providing a power lower than standard values (e.g. 70%). Some examples and an application in the cardiovascular area, including sensitivity analyses of the results based on the so-called Bayesian “assurance” technique, are also discussed. Several possible extensions of the model related to particular clinical frameworks are also presented.