New insights on the relation between untreated and treated outcomes for a given therapy effect model is not necessarily linear

Background and ObjectivesA relation between the size of treatment efficacy and severity of the disease has been postulated and observed as linear for a few therapies. We have called this relation the effect model. Our objectives were to demonstrate that the relation is general and not necessarily linear.Study Design and SettingWe extend the number of observed effect model. Then we established three numerical models of treatment activity corresponding to the three modes of action we have identified. Using these models, we simulated the relation.ResultsEmpirical evidence confirms the effect model and suggests that it may be linear over a short range of event frequency. However, it provides an incomplete understanding of the phenomenon because of the inescapable limitations of data from randomized clinical trials. Numerical modeling and simulation show that the real effect model is likely to be more complicated. It is probably linear only in rare instances. The effect model is general. It depends on factors related to the individual, disease and outcome.ConclusionContrarily to common, assumption, since the effect model is often curvilinear, the relative risk cannot be granted as constant. The effect model should be taken into account when discovering and developing new therapies, when making, health care policy decisions or adjusting clinical decisions to the patient risk profile.