Expected utility versus expected regret theory versions of decision curve analysis do generate different results when treatment effects are taken into account

Rationale, aims, and objectivesDecision curve analysis (DCA) is a widely used method for evaluating diagnostic tests and predictive models. It was developed based on expected utility theory (EUT) and has been reformulated using expected regret theory (ERG). Under certain circumstances, these 2 formulations yield different results. Here we describe these situations and explain the variation. MethodsWe compare the derivations of the EUT- and ERG-based formulations of DCA for a typical medical decision problem: treat none, treat all, or use model to guide treatment. We illustrate the differences between the 2 formulations when applied to the following clinical question: at which probability of death we should refer a terminally ill patient to hospice? ResultsBoth DCA formulations yielded identical but mirrored results when treatment effects are ignored; they generated significantly different results otherwise. Treatment effect has a significant effect on the results derived by EUT DCA and less so on ERG DCA. The elicitation of specific values for disutilities affected the results even more significantly in the context of EUT DCA, whereas no such elicitation was required within the ERG framework. ConclusionEUT and ERG DCA generate different results when treatment effects are taken into account. The magnitude of the difference depends on the effect of treatment and the disutilities associated with disease and treatment effects. This is important to realize as the current practice guidelines are uniformly based on EUT; the same recommendations can significantly differ if they are derived based on ERG framework.