Cost-effectiveness Analysis with Influence Diagrams

Abstract

Introduction: Cost-effectiveness analysis (CEA) is used increasingly in medicine to determine whether the health benefit of an intervention outweighs the economic cost. Decision trees, the standard decision modeling technique for non-temporal domains, can only perform CEAs for very small problems. Objective: To develop a method for CEA in problems involving several dozen variables. Methods: We explain how to build influence diagrams (IDs) that explicitly represent cost and effectiveness. We propose an algorithm for evaluating cost-effectiveness IDs directly, i.e., without expanding an equivalent decision tree. Results: The evaluation of an ID returns a set of intervals for the willingness to pay-separated by incremental cost-effectiveness ratios (ICERs)-and, for each interval, the cost, the effectiveness, and the optimal intervention. The algorithm that evaluates the ID directly is in general much more efficient than the brute-force method, which is in turn more efficient than the expansion of an equivalent decision tree. Using OpenMarkov, an open-source software tool that implements this algorithm, we have been able to perform CEAs on two medical IDs whose equivalent decision trees contain billions of branches. Conclusion: IDs can perform CEA on many problems that could not be analyzed with decision trees.

Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine

“…17 Decision trees are the most commonly used tool to model decision problems; however, their complexity grows exponentially with problem size; thus, they are tractable only when they contain fewer than a handful of variables. 19 An influence diagram can represent the same problems as a decision tree, but its size grows only linearly with the problem size, allowing for modeling of more complex decision problems. This is important as clinicians and decision makers must assess the best treatments most likely to be effective for a patient, while also considering the trade-offs between possible benefits of therapy and potential loss of quality of life.…”

“…Influence diagrams containing utility nodes can be used to conduct costeffectiveness or cost-utility analysis. 19 Value of information (VOI) analysis can be incorporated into BNs to streamline data collection and inform both the diagnosis and prognosis in an individual patient case, as well as future research priorities. Value of information analysis works by estimating the effect of observing a variable more precisely (reducing its uncertainty to zero) on some target variable of interest, on making a specific decision, or on cost (which can also be incorporated into BNs as utility nodes).…”

Eliciting and Exploiting Utility Coefficients in an Integrated Environment for Shared Decision-Making

“…However, also influence diagrams could be used, and some literature suggests that they may be more flexible in managing multiple time-distributed decisions. 30,31 Thus, we will evaluate the possibility to integrate UceWeb with tools for running such models. An additional limitation is that TreeAge Pro is a commercial product that requires a license.…”

Decision analytic modeling for the economic analysis of proton radiotherapy for non-small cell lung cancer

“…Influence Diagrams are used in many fields and are useful for integrating knowledge from disparate sources, exploring the effects of different decision variables, and determining information needs in order to make an optimal decision (16). Influence diagrams, which have long been used for decision modeling in health care (17), are gaining in popularity for cost-effectiveness analysis (18) and decision analysis in radiotherapy (19).…”