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Open Access Highly Accessed Research article

A regret theory approach to decision curve analysis: A novel method for eliciting decision makers' preferences and decision-making

Athanasios Tsalatsanis1, Iztok Hozo2, Andrew Vickers3 and Benjamin Djulbegovic14*

Author Affiliations

1 Center for Evidence-based Medicine and Health Outcomes Research, University of South Florida, Tampa, FL, USA

2 Department of Mathematics, Indiana University Northwest, Gary, IN, USA

3 Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, NY, NY, USA

4 H. Lee Moffitt Cancer Center& Research Institute, Tampa, FL, USA

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BMC Medical Informatics and Decision Making 2010, 10:51  doi:10.1186/1472-6947-10-51

Published: 16 September 2010



Decision curve analysis (DCA) has been proposed as an alternative method for evaluation of diagnostic tests, prediction models, and molecular markers. However, DCA is based on expected utility theory, which has been routinely violated by decision makers. Decision-making is governed by intuition (system 1), and analytical, deliberative process (system 2), thus, rational decision-making should reflect both formal principles of rationality and intuition about good decisions. We use the cognitive emotion of regret to serve as a link between systems 1 and 2 and to reformulate DCA.


First, we analysed a classic decision tree describing three decision alternatives: treat, do not treat, and treat or no treat based on a predictive model. We then computed the expected regret for each of these alternatives as the difference between the utility of the action taken and the utility of the action that, in retrospect, should have been taken. For any pair of strategies, we measure the difference in net expected regret. Finally, we employ the concept of acceptable regret to identify the circumstances under which a potentially wrong strategy is tolerable to a decision-maker.


We developed a novel dual visual analog scale to describe the relationship between regret associated with "omissions" (e.g. failure to treat) vs. "commissions" (e.g. treating unnecessary) and decision maker's preferences as expressed in terms of threshold probability. We then proved that the Net Expected Regret Difference, first presented in this paper, is equivalent to net benefits as described in the original DCA. Based on the concept of acceptable regret we identified the circumstances under which a decision maker tolerates a potentially wrong decision and expressed it in terms of probability of disease.


We present a novel method for eliciting decision maker's preferences and an alternative derivation of DCA based on regret theory. Our approach may be intuitively more appealing to a decision-maker, particularly in those clinical situations when the best management option is the one associated with the least amount of regret (e.g. diagnosis and treatment of advanced cancer, etc).