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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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).</p

Regret affects the choice between neoadjuvant therapy and upfront surgery for potentially resectable pancreatic cancer

Background: When treating potentially resectable pancreatic adenocarcinoma, therapeutic decisions are left to the sensibility of treating clinicians who, faced with a decision that post hoc can be proven wrong, may feel a sense of regret that they want to avoid. A regret-based decision model was applied to evaluate attitudes to-ward neoadjuvant therapy versus upfront surgery for potentially resectable pancreatic adenocarcinoma.Methods: Three clinical scenarios describing high-, intermediate-, and low-risk disease-specific mortality after upfront surgery were presented to 60 respondents (20 oncologists, 20 gastroenterologists, and 20 surgeons). Respondents were asked to report their regret of omission and commission regarding neo-adjuvant chemotherapy on a scale between 0 (no regret) and 100 (maximum regret). The threshold model and a multilevel mixed regression were applied to analyze respondents' attitudes toward neo-adjuvant therapy.Results: The lowest regret of omission was elicited in the low-risk scenario, and the highest regret in the high-risk scenario (P &lt; .001). The regret of the commission was diametrically opposite to the regret of omission (P &lt; .001). The disease-specific threshold mortality at which upfront surgery is favored over the neoadjuvant therapy progressively decreased from the low-risk to the high-risk scenarios (P &lt;=.001). The nonsurgeons working in or with lower surgical volume centers (P = .010) and surgeons (P = .018) accepted higher disease-specific mortality after upfront surgery, which resulted in the lower likelihood of adopting neoadjuvant therapy.Conclusion: Regret drives decision making in the management of pancreatic adenocarcinoma. Being a surgeon or a specialist working in surgical centers with lower patient volumes reduces the likelihood of recommending neoadjuvant therapy.(c) 2023 Elsevier Inc. All rights reserved

Decision curve analysis revisited: overall net benefit, relationships to ROC curve analysis, and application to case-control studies

ABSTRACT: BACKGROUND: Decision curve analysis has been introduced as a method to evaluate prediction models in terms of their clinical consequences if used for a binary classification of subjects into a group who should and into a group who should not be treated. The key concept for this type of evaluation is the "net benefit", a concept borrowed from utility theory. METHODS: We recall the foundations of decision curve analysis and discuss some new aspects. First, we stress the formal distinction between the net benefit for the treated and for the untreated and define the concept of the "overall net benefit". Next, we revisit the important distinction between the concept of accuracy, as typically assessed using the Youden index and a receiver operating characteristic (ROC) analysis, and the concept of utility of a prediction model, as assessed using decision curve analysis. Finally, we provide an explicit implementation of decision curve analysis to be applied in the context of case-control studies. RESULTS: We show that the overall net benefit, which combines the net benefit for the treated and the untreated, is a natural alternative to the benefit achieved by a model, being invariant with respect to the coding of the outcome, and conveying a more comprehensive picture of the situation. Further, within the framework of decision curve analysis, we illustrate the important difference between the accuracy and the utility of a model, demonstrating how poor an accurate model may be in terms of its net benefit. Eventually, we expose that the application of decision curve analysis to case-control studies, where an accurate estimate of the true prevalence of a disease cannot be obtained from the data, is achieved with a few modifications to the original calculation procedure. CONCLUSIONS: We present several interrelated extensions to decision curve analysis that will both facilitate its interpretation and broaden its potential area of application

Patients' values and preferences of the expected efficacy of hip arthroscopy for osteoarthritis : a protocol for a multinational structured interview-based study combined with a randomised survey on the optimal amount of information to elicit preferences

Peer reviewe

Predicting Academic Performance: A Systematic Literature Review

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.Peer reviewe

Control of Autonomous Robot Teams in Industrial Applications

The use of teams of coordinated mobile robots in industrial settings such as underground mining, toxic waste cleanup and material storage and handling, is a viable and reliable approach to solving such problems that require or involve automation. In this thesis, abilities a team of mobile robots should demonstrate in order to successfully perform a mission in industrial settings are identified as a set of functional components. These components are related to navigation and obstacle avoidance, localization, task achieving behaviors and mission planning. The thesis focuses on designing and developing functional components applicable to diverse missions involving teams of mobile robots; in detail, the following are presented: 1. A navigation and obstacle avoidance technique to safely navigate the robot in an unknown environment. The technique relies on information retrieved by the robot\u27s vision system and sonar sensors to identify and avoid surrounding obstacles. 2. A localization method based on Kalman filtering and Fuzzy logic to estimate the robot\u27s position. The method uses information derived by multiple robot sensors such as vision system, odometer, laser range finder, GPS and IMU. 3. A target tracking and collision avoidance technique based on information derived by a vision system and a laser range finder. The technique is applicable in scenarios where an intruder is identified in the patrolling area. 4. A limited lookahead control methodology responsible for mission planning. The methodology is based on supervisory control theory and it is responsible for task allocation between the robots of the team. The control methodology considers situations where a robot may fail during operation. The performance of each functional component has been verified through extensive experimentation in indoor and outdoor environments. As a case study, a warehouse patrolling application is considered to demonstrate the effectiveness of the mission planning component