Quantitative Decision Analysis Research Articles

One Health Risk and Disease (OHRAD): a tool to prioritise the risks for epidemic-prone diseases from One Health perspective

BackgroundThe rise in epidemic-prone diseases daily poses a serious concern globally. Evidence suggests that many of these diseases are of animal origin and contribute to economic loss. Considering the limited time and other resources available for the animal and human health sectors, selecting the most urgent and significant risk factors and diseases is vital, even though all epidemic-prone diseases and associated risk factors should be addressed. The main aim of developing this tool is to provide a readily accessible instrument for prioritising risk factors and diseases that could lead to disease emergence, outbreak or epidemic.MethodsThis tool uses a quantitative and semi-quantitative multi-criteria decision analysis (MCDA) method that involves five steps: Identifying risk factors and diseases, Weighting the criteria, Risk and disease scoring, Calculating risk impact and disease burden score, and Ranking risks and diseases. It is intended to be implemented through a co-creation workshop and involves individual and group activities. The last two steps are automated in the MS Excel score sheet.ResultsThis One Health Risk and Disease (OHRAD) prioritisation tool starts with an individual activity of identifying the risks and diseases from the more extensive list. This, then, leads to a group activity of weighing the criteria and providing scores for each risk and disease. Finally, the individual risk and disease scores with the rankings are generated in this tool.ConclusionsThe outcome of this OHRAD prioritisation tool is that the top risks and diseases are prioritised for the particular context from One Health perspective. This prioritised list will help experts and officials decide which epidemic-prone diseases to focus on and for which to develop and design prevention and control measures.

Software Quality Assessment Approach using Analytical Hierarchical Model: Applied in SMEs

Continuous software quality evolution is crucial albeit challenging due to tight budgets and timelines. In fact, neglecting software quality in favour of customer satisfaction; either by reducing internal or external defects or by adding technical features shall place much pressure on project managers and stakeholders to find a balance between project budget and schedule. The increasing demand for high-quality software among stakeholders emphasizes the need for comprehensive approaches that can evaluate and rank quality metrics to maximize their benefits. In this research paper, we introduce an assessment approach developed using key quality metrics integrated from Cost Of Software Quality (COSQ) metrics, reliability metrics, and cost of defects metrics based on quantitative multicriteria decision analysis capabilities of the Analytic Hierarchy Process (AHP) model applied with a real case study for the software development team in Small and Medium Enterprises (SMEs) since most of them do not follow standards nor adopt well-structured quality methods. The evaluation is established based on the integrated AHP assessment approach as a practical solution for organizations seeking a critical examination of the relative significance of software metrics in terms of their utility in enhancing business performance. As a result, an evaluation of these quality metrics has been conducted to highlight the percentage of weight for each quality metric based on interviews with subject matters experts. The ranking of software quality metrics greatly helps stakeholders in selecting the most appropriate attribute for evaluating the developed software to identify high-impact quality initiatives and measure their effectiveness in the Software Development Life Cycle (SDLC)

A study of gold and bitcoin trading strategies based on quantitative trading decision models

In the computer era, high frequency trading is one of the hottest topics in the financial investment market. In this paper, a quantitative trading decision analysis is conducted for a portfolio consisting of gold and bitcoin, applying gray prediction to get the future asset price, applying time series to predict the risk of purchasing the asset, and combining the two to make a reasonable buying and selling operation. To prove the accuracy of the prediction, the MSE indicator is used to measure the error of the gray prediction model, and the results show that the error is small. Finally this paper also proves that the model is well robust by varying the transaction cost.

The Use of Evidence-Informed Deliberative Processes for Health Benefit Package Design in Kazakhstan.

Kazakhstan strives to obtain Universal Health Coverage (UHC) by using health technology assessment (HTA) for determining their health benefit package. This paper reports on employing evidence-informed deliberative processes (EDPs), a practical and stepwise approach to enhance legitimate health benefit package design in Kazakhstan. Methods: The Ministry of Health of Kazakhstan approved the operationalization and application of EDPs during March 2019 and December 2020. We used a combination of desk research, conducting HTA, online surveys as well as a face-to-face workshop in Nur-Sultan, Kazakhstan, and two online workshops to prioritize 25 selected health technologies. During the latter, we tested two alternative approaches to prioritization: quantitative multicriteria decision analysis (MCDA) and the use of decision rules. Results: For each of the HTA reports, evidence summaries were developed according to the decision criteria (safety, social priority disease, severity of disease, effectiveness, cost-effectiveness, level of evidence, financial risk protection and budget impact). When appraising the evidence, the advisory committee preferred using quantitative MCDA, and only when this would result in any controversy could decision rules be applied. Conclusions: Despite several challenges, including a partial disruption because of the COVID-19 pandemic, implementation of the process will likely play a key role in determining an evidence-informed and transparent health benefit package.

A Cross-Sectional Decision-Making Approach to Inform Neuropsychological Battery Development in Professional Hockey.

Neuropsychologists commonly use a large battery of tests to inform clinical decisions. Decision analysis can be used to determine which individual tests play a role in the decision-making process. The objective of this project was to conduct quantitative and qualitative decision analysis of decisions by team neuropsychologists with professional hockey players being evaluated as part of the National Hockey League (NHL)/NHL Players Association Concussion Protocol. We extracted neuropsychological data from an NHL clinical program database. Team neuropsychologists evaluated concussed players using a hybrid neuropsychological test battery. The neuropsychologists then determined whether players were experiencing concussion-related cognitive difficulties. Logistic regression was used to examine which tests accounted for unique variance in the decision-making process. We also conducted a survey of NHL neuropsychologists, asking them to rate the usefulness of each test in the battery. Five of the fifteen measures accounted for unique variance in team neuropsychologists' decisions, including the ImPACT Verbal Memory Composite, Visual Motor Composite, Reaction Time Composite, Symptom Score, and Brief Visuospatial Memory Test-Revised Delayed Recall. Notable discrepancies were uncovered between quantitative indications of usefulness and self-reported qualitative perceptions of test usefulness when making decisions. Qualitatively, clinicians reported that the Hopkins Verbal Learning Test-Revised, Symbol Digit Modalities Test, ImPACT Reaction Time, and Color Trails 2 were the most useful tests when making decisions. Along with validation studies, decision analysis can be used as part of a comprehensive evaluation process to inform the development of best-practice batteries for use among athletes with sports concussion.

Quantitative Decision Analysis for Co2 Storage Conformance Management: A Synthetic Case Study at Smeaheia, North Sea

In this work, we propose a workflow for quantifying the value of a candidate monitoring strategy for conformance management decisions. A value of information (VOI) analysis framework is constructed by combining a quantitative conformance verification workflow and an economic model via decision tree analysis. The presented workflow is applied to a synthetic case study based on the Smeaheia storage aquifer, where the uncertainty on the transmissibility of an important fault poses a risk to the conformance of the reservoir pressure behaviour. By incorporating information from candidate monitoring configurations to better constrain the forecasts of the ensemble of model realizations used to characterize the inherent geological uncertainties, an assessment of the time-dependent ability of detecting deviations from conforming behaviour is achieved and followed by an economic evaluation of the considered alternatives for pressure control actions. Using data-driven approximations, the analysis can be repeated for several monitoring configurations (type of measurement, location, measurement error and timing of measurement) accounting for plausible outcomes of the analysed measurements. This allows us to determine the expected value of the storage operation for each monitoring configuration, and for possible actions available to the operator for remediating unwanted storage site development. The results obtained show that the value achieved from monitoring depends on both the timing and information content of the measurements. Direct and indirect (imperfect) observations of the quantity of interest for conformance might not always be sufficient to guarantee perfectly accurate conformance statements and conformance management decisions. This highlights the practical value of addressing decision problems in quantitative terms to support the design of CO2 storage systems by gaining insight into the effectiveness of monitoring and corrective actions.

ICER Value Framework 2020 Update: Recommendations on the Aggregation of Benefits and Contextual Considerations.

The Institute for Clinical and Economic Review (ICER) in the United States recently published a 2020 update to its value assessment framework. We are commenting on the method by which the benefits of health interventions are integrated, relating to contextual considerations and other factors relevant to an intervention's value. We start by discussing the theoretical foundations of decision analysis and its extension to multiple criteria decision analysis (MCDA). Then we provide a detailed, evidence-based response to some of the claims made by ICER with regard to the use of MCDA methods and stakeholder engagement. Finally, we provide a number of recommendations on the use of quantitative decision analysis and decision conferencing that could be of relevance to the ICER methodology. Overall, we agree that some of the proposed changes by ICER are moving in the right direction toward improving transparency in the value assessment process, but these changes are probably inadequate. We advocate that more serious attention should be paid to the use of quantitative decision analysis together with decision conferencing for the construction of value preferences via group processes for the integration of an intervention's various benefit components.

Impact of Digitalization on the Way of Working and Skills Development in Hydrocarbon Production Forecasting and Project Decision Analysis

SummaryHydrocarbon (re-)development projects need to be evaluated under uncertainty. Forecasting oil and gas production needs to capture the ranges of the multitude of uncertain parameters and their impact on the forecast to maximize the value of the project for the company. Several authors showed, however, that the oil and gas industry has challenges in adequately assessing the distributions of hydrocarbon production forecasts.The methods for forecasting hydrocarbon production developed with digitalization from using analytical solutions to numerical models with an increasing number of gridblocks (“digital twins”) toward ensembles of models covering the uncertainty of the various parameters. Analytical solutions and single numerical models allow calculation of incremental production for a single case. However, neither the uncertainty of the forecasts nor the question in which the distribution of various outcomes the single model is located can be determined. Ensemble-based forecasts are able to address these questions, but they need to be able to cover a large number of uncertain parameters and the amount of data that is generated accordingly.Theory-guided data science (TGDS) approaches have recently been used to overcome these challenges. Such approaches make use of the scientific knowledge captured in numerical models to generate a sufficiently large data set to apply data science approaches. These approaches can be combined with economics to determine the desirability of a project for a company (expected utility). Quantitative decision analysis, including a value of information (VoI) calculation, can be done addressing the uncertainty range but also the risk hurdles as required by the decision-maker (DM). The next step is the development of learning agent systems (agent: autonomous, goal-directed entity that observes and acts upon an environment) that are able to cope with the large amount of data generated by sensors and to use them for conditioning models to data and use the data in decision analysis.Companies need to address the challenges of data democratization to integrate and use the available data, organizational agility, and the development of data science skills but making sure that the technical skills, which are required for the TGDS approach, are kept.

Advancing structured decision-making in drug regulation at the FDA and EMA.

The recent benefit-risk framework (BRF) developed by the Food and Drug Administration (FDA) is intended to improve the clarity and consistency in communicating the reasoning behind the FDA's decisions, acting as an important advancement in US drug regulation. In the PDUFA VI implementation plan, the FDA states that it will continue to explore more structured or quantitative decision analysis approaches; however, it restricts their use within the current BRF that is purely qualitative. By contrast, European regulators and researchers have been long exploring the use of quantitative decision analysis approaches for evaluating drug benefit-risk balance. In this paper, we show how quantitative modelling, backed by decision theory, could complement and extend the FDA's BRF to better support the appraisal of evidence and improve decision outcomes. After providing relevant scientific definitions for benefit-risk assessment and describing the FDA and European Medicines Agency (EMA) frameworks, we explain the components of and differences between qualitative and quantitative approaches. We present lessons learned from the EMA experience with the use of quantitative modelling and we provide evidence of its benefits, illustrated by a real case study that helped to resolve differences of judgements among EMA regulators.

PCN325 ENGAGING PATIENTS TO ELICIT CRITERIA IMPORTANT FOR VALUING THERAPIES

Pharmaceutical Value (pValue), a center of excellence funded by PhRMA Foundation in novel value assessment, partnered with the Cancer Support Community to gain patient insights into the importance of value assessment criteria. Prior to developing quantitative multicriteria decision analysis (MCDA) tools for value assessment interpretation, we conducted preliminary evidence synthesis and generation exercises to identify and understand the importance of value criteria. The objective of this study was to engage cancer patients to elicit the criteria that are most important toward their judgements related to the value of therapies.

Relating Optimization Problems to Systems of Inequalities and Equalities

In quantitative decision analysis, an analyst applies mathematical models to make decisions. Frequently these models involve an optimization problem to determine the values of the decision variables, a system S of possibly non- linear inequalities and equalities to restrict these variables, or both. In this note, we relate a general nonlinear programming problem to such a system S in such a way as to provide a solution of either by solving the other—with certain limitations. We first start with S and generalize phase 1 of the two-phase simplex method to either solve S or establish that a solution does not exist. A conclusion is reached by trying to solve S by minimizing a sum of artificial variables subject to the system S as constraints. Using examples, we illustrate how this approach can give the core of a cooperative game and an equilibrium for a noncooperative game, as well as solve both linear and nonlinear goal programming problems. Similarly, we start with a general nonlinear programming problem and present an algorithm to solve it as a series of systems S by generalizing the “sliding objective function method” for two-dimensional linear programming. An example is presented to illustrate the geometrical nature of this approach.

PNS198 DEVELOPMENT OF EARLY DECISION ANALYTICS (DA) PLATFORM TO SUPPORT RESEARCH AND DEVELOPMENT MILESTONES WITH ANTICIPATED REAL-WORLD VALUE

Pharmaceutical development is lengthy, costly and uncertain with a dynamically changing competitive landscape. An economic evaluation that integrates burden of disease, patient characteristics, desired target profile and a linking to real-world evidence can be very important. This study aims to characterize a visual, intuitive and easy-to-use decision analytics platform. A survey of features of existing DA platforms relevant to problems within discovery and early development was conducted. Also, problems that are encountered in pharmaceuticals development with their inputs, desired outputs, key sources of information and stakeholders were also enumerated. The software tools were then scored against these types of problems. Those ranged from an evaluation for unmet medical need in a given disease, compound selection point, patient population selection, comparative effectiveness prediction, outcomes-based pricing decision support and portfolio analysis. Metrics include: 1) ease of use 2) GUI 3) analytics 4) diversity of problems 5) ease to import and link to external data 6) technical documentation. A final score integrating all features was then derived. The software platforms included health economics and general decision ones (HERO 3.0, TreeAge, EviDICE, DPL, HOPE). None met all imposed criteria. We found the translatability from a graphical representation to analytics, the flexibility to handle different problems scoring different metrics and the handling of uncertainty to be most important. A prototype including these metrics was created. Quantitative decision analysis is important as a guide in the early stages of pharmaceutical R&D. The existing analytic tools and methods have some but not all desired characteristics to support fast and seamless decision analysis. This study highlights key features and proposes a prototype of such a tool.

Decision-making Model of Virtual Power Plant for Participating in Spot Market Transaction Based on Hybrid Stochastic and Robust Approach

China is vigorously promoting the reform of the electricity spot market after the notice on the development of pilot projects for the spot electricity market was issued in 2017. At the same time, china is upgrading and renovating its energy structure, in the context of structural reform on the energy supply side, the decentralized form of clean energy utilization will develop rapidly. With the continuous improvement of the trading mechanism in spot market, it has become an inevitable trend that many distributed power resources will be involved in electricity market to participate in market transaction. Therefore, in order to promote distributed energy to participate in spot market, virtual power plant technique is paid increasing attentions. Combining the current hot issue, this paper constructs a decision-making model of virtual power plant for participating in spot market transaction based on hybrid stochastic and robust method, which can provide a quantitative decision analysis tool for virtual power plant operators to participate in spot market transactions. The main contribution of this paper are as follows:1) we proposed a transaction decision model that based on hybrid stochastic optimization and robust optimization methods and example simulation was given to illustrate the effectiveness of the model; 2) this paper focused on the electricity market in china.

Komunikowanie polityczne Konferencji Episkopatu Polski po zakończonych wyborach prezydenckich 2015 r. Priming i framing – analiza tygodników opinii „Newsweek Polska” i „Polityka”

<p>Zagadnienia podjęte w tekście mają zasadniczo swoje odniesienie do nauk o polityce oraz nauk o mediach. Artykuł jest próbą odpowiedzi na pytanie, czy i w jaki sposób polskie tygodniki opinii „Newsweek Polska” i „Polityka” wykorzystały funkcję pozycjonowania i ramowania w przekazach na temat komunikowania politycznego Konferencji Episkopatu Polski (KEP) w okresie od wyboru Andrzeja Dudy na urząd prezydenta Rzeczypospolitej do czasu jego zaprzysiężenia. Podjęte badanie jest analizą praktycznego zastosowania koncepcji priming i framing w komunikowaniu politycznym Kościoła hierarchicznego w Polsce. W tekście przywołano komunikaty instytucjonalne KEP wyrażające zaangażowanie polityczne biskupów oraz echa prasowe tychże komunikatów. Autor posłużył się: metodą ilościową, metodą analizy zawartości oraz metodą analizy decyzyjnej. Dokonał szczegółowej analizy łącznie 20 tekstów prasowych, które spełniły przyjęte kryteria metodologiczne. Artykuł ukazuje istotną rolę, jaką odgrywają media w procesach komunikowania politycznego. Podjęte badania jakościowe, na podstawie koncepcji ram interpretacyjnych, pomogły we właściwej hermeneutyce otrzymanych wyników empirycznych. Autor nie pominął również badań ilościowych. Biskupi polscy zostali zaprezentowani jako podmiot polityczny. Przekazy instytucjonalne KEP są zgodne z katolicką nauką społeczną dotyczącą zaangażowania Kościoła w politykę, z kolei tygodniki opinii komunikowanie polityczne KEP ukazały jako wykraczające poza ramy Magisterium Kościoła.</p>

A quantified Kraljic Portfolio Matrix: Using decision analysis for strategic purchasing

Since its inception, the Kraljic Portfolio Matrix (KPM) has been widely used as a diagnostic and prescriptive purchasing tool. One of the primary weaknesses of the KPM is the qualitative nature of the model, which results in a subjective method for weighting and positioning suppliers or commodities in the various quadrants. In an effort to help overcome this widely cited weakness, this research proposes an objective, quantitative decision analysis approach to position products and services within the KPM. Single attribute value functions are used to quantify the strategic purchasing attributes of the KPM. A multi attribute value function is then used to quantify and rank-order products and services within the KPM based on the two strategic purchasing objectives. We empirically demonstrate this approach using data from an organization with a $10 billion purchasing portfolio. Furthermore, we illustrate how a cost-benefit view of the results help decision makers quickly and easily identify the products and services most conducive to the implementation of commercial best practice purchasing strategies. A sensitivity analysis advocates the robustness of our approach.

Remediation Decision‐Making and Behavioral Economics: Results of an Industry Survey

AbstractDecision methods applied in the remediation industry were evaluated using a survey of industry practitioners, in order to assess the relative roles of quantitative decision analysis and gut intuition. Principals from the disciplines of behavioral economics and decision theory were used as a framework to evaluate remediation decision behaviors revealed by the survey. The survey was completed by 118 respondents representing academia, consultants, clients, and others. Survey questions focused on perceptions and experiences related to inputs to decisions and decision processes, as well as remediation goal setting and outcomes. The most common remediation objective cited was short‐term interim measures and the least common was no further action (NFA) with clean closure. NFA was also sparingly achieved: 33% of respondents reported zero NFA closures in their career, and an overall 15% to 20% NFA closure rate was reported among more experienced respondents. Data inputs were ranked most important to decisions, while the decision process itself was ranked lowest. Intuition‐based decision methods such as asking for a trusted opinion, rules of thumb, and meetings were all used at last twice as often as decision analysis such as discounted cash flow or probabilistic analysis. Analysis of survey responses showed that cognitive biases, including overconfidence effect and intuition bias, are present to some extent in remediation decision‐making. Practitioners are advised to be mindful of the decision‐making processes they apply, and to incorporate elements of both intuition and decision analysis, as appropriate to the decisions being made.

Two additional principles for determining which species to monitor.

Monitoring to detect population declines is widespread, but also costly. There is, consequently, a need to optimize monitoring to maximize cost-effectiveness. Here we develop a quantitative decision analysis framework for how to optimally allocate resources for monitoring among species. By keeping the framework simple, we analytically establish two new principles about which species are optimal to monitor for detecting declines: (1) those that lie on the boundary between species being allocated resources for conservation action and species that are not and (2) those with the greatest uncertainty in whether they are declining. These two principles are in addition to other factors that are also important in monitoring decisions, such as complementarity. We demonstrate the efficacy of these principles when other factors are not present, and show how the two principles can be combined. This analysis demonstrates that the most cost-effective species to monitor are ones where the information gained from monitoring is most likely to change the allocation of funds for action, not necessarily the most vulnerable or endangered. We suggest these results are general and apply to all ecological monitoring, not just of biological species: monitoring and information are only valuable when they are likely to change how people act.

To test or not to test … decision analysis of decision support.

To test or not to test … decision analysis of decision support.

Integrated Scenario Planning and Multi-Criteria Decision Analysis Framework with Application to Forest Planning

This paper explores approaches concerning complex forest planning challenges, such as restoration after large-scale disturbances and under climate change. It introduces a new framework that integrates qualitative scenario planning with quantitative multi-criteria decision analysis. This framework allows stakeholders without background in forestry to express their preferences as a set of scenarios that are further assessed for specific forest management goals and activities using multi-criteria models. The assessment of the modelled scenarios created a common understanding for the stakeholders and experts to compare trade-offs between several management options and needed policy choices. The framework was applied in the case study of forest restoration following insect disturbance in British Columbia, Canada. The framework enabled structured stakeholder groups’ interactions such as industry, business associations, local and regional governments, and non-governmental organizations to identify potential restoration options. Different community futures were envisioned by two scenarios: one resembling current conditions and standard practices, while another promoting diversification of the forestry sector. The results indicated that each of the scenarios leads to different consequences for the community measured by levels of economic benefits, total harvest volumes and harvest flows over time. The results also show that the developed framework linking scenarios and multi-criteria decision analyses proved crucial to broaden the discussion on relevant species mixes and management practices, and their implications for the community and policy development.

Development of Improved caprock Integrity Analysis and Risk Assessment Techniques

Abstract GeoMechanics Technologies has completed a geomechanical caprock integrity analysis and risk assessment study funded through the US Department of Energy. The project included: a detailed review of historical caprock integrity problems experienced in the natural gas storage industry; advanced coupled transport flow modelling and geomechanical simulation of three large-scale potential geologic sequestration sites to estimate geomechanical effects from large-scale CO2 injection; and development of a quantitative risk and decision analysis tool to assess caprock integrity risks. Historical data from gas storage operations and CO2 sequestration projects suggest that leakage and containment incident risks are on the order of 10-1 to 10-2, which is higher risk than some previous studies have suggested for CO2. Geomechanical analysis, as described herein, can be applied to quantify risks and to provide operating guidelines to reduce risks. The risk assessment tool developed for this project has been applied to five areas: The Wilmington Graben offshore Southern California, Kevin Dome in Montana, the Louden Field in Illinois, the Sleipner CO2 sequestration operation in the North Sea, and the In Salah CO2 sequestration operation in North Africa. Of these five, the Wilmington Graben setting represent the highest relative risk while the Kevin Dome setting represents the lowest relative risk.