A cost-effectiveness and value of information analysis to inform future research of tranexamic acid for older adults experiencing mild traumatic brain injury

Background Tranexamic acid reduces head injury deaths in patients with CT scan evidence of intracranial bleeding after mild traumatic brain injury (TBI). However, the cost-effectiveness of tranexamic acid for people with mild TBI in the pre-hospital setting, prior to CT scanning, is uncertain. A large randomised controlled trial (CRASH-4) is planned to address this issue, but the economic justification for it has not been established. The aim of the analysis was to estimate the likelihood of tranexamic acid being cost-effective given current evidence, the treatment effects required for cost-effectiveness, and the expected value of performing further research. Methods An early economic decision model compared usual care for mild TBI with and without tranexamic acid, for adults aged 70 and above. The evaluation was performed from a UK healthcare perspective over a lifetime time horizon, with costs reported in 2020 pounds (GBP) and outcomes reported as quality adjusted life years (QALYs). All analyses used a £20,000 per QALY cost-effectiveness threshold. Results In the base case analysis, tranexamic acid was associated with an incremental cost-effectiveness ratio of £4,994 per QALY gained, and was 85% likely to be cost-effective in the base case probabilistic sensitivity analysis. The value of perfect information was £13.2 million, and the value of perfect information for parameters that could be collected in a trial was £12.4 million. The all-cause mortality risk ratio for tranexamic acid and the functional outcomes following TBI had the most impact on cost-effectiveness. Conclusions Tranexamic acid can be cost-effective at a very modest treatment effect, and there is a high value of performing future research in the UK. The value in a global context is likely to be far higher.

Agroforestry And Reforestation With The Gold Standard - Decision Analysis of A Voluntary Carbon Offset Label

Voluntary standards help to ensure the quality of projects eligible for carbon offsetting, i.e. selling carbon certificates. However, in deciding on whether to adopt such standards the managers of carbon offset projects are faced with uncertainty regarding the costs and risks involved. Decision Analysis provides a helpful set of tools that can support such decisions by forecasting outcomes under different scenarios. We applied Decision Analysis methods to generate models for the decision to certify two projects in Costa Rica with the voluntary carbon offset label Gold Standard. We evaluated certifying an additional site of a partially certified reforestation project, as well as the initial certification of an agroforestry project. We calibrated and interviewed decision-makers and stakeholders of the certification projects to identify important parameters and translated these into a decision model. We ran the final decision model as a Monte Carlo simulation to project plausible ranges of decision outcomes, expressed as Net Present Values and annual cash flows. We identified critical uncertainties and research priorities by using the Expected Value of Perfect Information. The results indicate that certification of the two projects would result in a positive Net Present Value. The partially low return on investment of the certification, however, shows the need for projects to undergo thorough evaluation and generate customized strategies before participating in a voluntary carbon offset scheme. The Decision Analysis approaches we describe can help to improve the process of decision making under uncertainty and should be widely adopted for evaluating the potential impacts of certification.

The importance of social values in the prioritization of research: a quantitative example and generalizations

1. Identifying critical uncertainties about ecological systems can help prioritize research efforts intended to inform management decisions. However, exclusively focusing on the ecological system neglects the objectives of natural resource managers and the associated social values tied to risks and rewards of actions. 2. I demonstrate how to prioritize research efforts for a harvested population by applying expected value of perfect information (EVPI) analysis to a matrix projection model of steelhead (Oncorhynchus mykiss) and an explicit utility function that models risk/reward objectives. Research priorities identified by EVPI diverge from priorities identified by matrix elasticity analyses that ignore utility. The degree of divergence depends on uncertainty in population vital rates and the particular form of the utility function used to represent risk/reward of harvest. 3. Synthesis and applications. EVPI analysis that includes perceived utility of different outcomes should be used by managers seeking to optimize monitoring and research spending. Collaboration between applied ecologists and social scientists that quantitatively measure peoples’ values is needed in many structured decision making processes.

Value of Improved Information about Environmental Protection Values: Toward a Benefit–Cost Analysis of Public-Good Valuation Studies

What is the benefit from obtaining more precise values of environmental or other public goods through surveys or other information gathering? In the value of information (VOI) problem studied here, a buyer who wishes to preserve a resource sets a price to offer a seller without knowing precisely its protection value, B, nor its value to the seller, V. The VOI from more precise information about B is important for environmental and natural resource valuation, but is typically not quantified nor compared to valuation costs. More precise environmental values reduce the frequency of two types of mistakes (protecting the resource when it should not be; and not protecting it when it should), and increases ex ante welfare. We apply our analysis to Amazon rainforest protection, focusing on the “value of perfect information,” VOPI, which, we show through simulations, typically exceeds realistic valuation costs, justifying significant valuation expenditures. VOPI also depends on the nature of buyer–seller interactions, and takes its highest value when the buyer has full concern for the seller’s outcome. Our paper proposes and prepares the base for a new, needed, field in applied welfare economics, the “benefit–cost analysis of public-good valuation studies.”

Quantitative Approaches to Representing the Value of Information Within the Intelligence Cycle

The authors propose that valuation of information metrics developed near the end of the intelligence cycle are appropriate supplemental metrics for national security intelligence. Existing information and decision theoretic frameworks are often either inapplicable in the context of national security intelligence or they capture affects from inputs aside from just the information or intelligence. Applied information theory looks at the syntactic transmission of information rather than assigning it a quantitative value. Information economics determines the market value of information, which is also inapplicable in a national security intelligence context. Decision analysis can use the value of information to show the expected value of perfect information (EVPI) and the expected value of imperfect information (EVII) and although this method can be used with utility theory and not just monetary objectives, it has been shown that decision makers within the intelligence community (IC) have difficulty agreeing upon how to value objectives within analysis. Additionally, it is difficult to determine how decision makers use intelligence in the decision-making process, which makes existing decision theoretic methods problematic, and might include inputs from variables besides just the intelligence.