Multi-criteria Decision Analysis Model Research Articles

PT240 - Benefit-risk evaluation of approved pharmacotherapies for the treatment of overactive bladder: A sensitivity analysis of benefits versus safety for individual OAB symptoms using multi-criteria decision analysis modelling

PT240 - Benefit-risk evaluation of approved pharmacotherapies for the treatment of overactive bladder: A sensitivity analysis of benefits versus safety for individual OAB symptoms using multi-criteria decision analysis modelling

Robust multi-criteria weighting in comparative LCA and S-LCA: A case study of sugarcane production in Brazil

This article presents a novel multi-criteria decision analysis (MCDA) model for performing robust indicator weighting in Life Cycle Assessment (LCA) and Social Life Cycle Assessment (S-LCA). This model integrates stochastic weights analysis with preference information that utilizes the value judgements of decision makers, benefitting from the diversity of interests and familiarities of decision makers regarding each indicator. The model considers all decision makers on an equal basis but does not assume they have the same importance. The MCDA model was applied to support the evaluation of the overall environmental and social impacts of manual and mechanical sugarcane harvesting in Brazil based on LCA and S-LCA. Brazilian experts were surveyed on the weights of relevant environmental and social indicators. The novel MCDA approach explores all the possible convex combinations of the weights provided by the surveyed group. The results of the MCDA model show that mechanical harvesting compared to manual harvesting had lower environmental life cycle impacts at the end-point level and better social impacts for all these convex combinations. Decision-making based on environmental impacts at the mid-point level is less clear: manual harvesting is more likely (67% of the convex combinations of the weights) to be considered better than mechanical harvesting; but the advantage of mechanical harvesting over manual harvesting can be greater than the reverse (almost twice as large). This article recommends presenting both mid-point and end-point LCIA results for a thoroughly informed decision-making. The MCDA model developed in this article can also be used to support weighting in future comparative LCA or S-LCA studies.

Benefit-risk Assessment of Cladribine Using Multi-criteria Decision Analysis (MCDA) for Patients With Relapsing-remitting Multiple Sclerosis

PurposeWe applied Multi-Criteria Decision Analysis (MCDA) methods in a structured benefit–risk assessment of cladribine and newer approved disease-modifying drugs (DMDs) for patients with relapsing-remitting multiple sclerosis (RRMS). MethodsDecision conferencing with clinical neurologists as decision makers was used to create an MCDA model that incorporated available evidence on DMDs for RRMS and clinical judgments about the relevance of the evidence. Benefit–risk assessments were conducted for DMDs in both patients with RRMS and patients with RRMS with high disease activity (HDA; defined as ≥2 relapses in the previous year). Treatment options included cladribine and recently approved DMDs available in European Union countries at the time of assessment (December 2015): alemtuzumab, dimethyl fumarate, fingolimod, natalizumab, and teriflunomide. To account for the relative importance of DMD effects, scores for the MCDA model were weighted to ensure that the most clinically important attributes carried more weight in the final benefit–risk calculation. The neurologists weighted different efficacy and safety profile attributes without any reference to individual DMDs to disassociate the assessment of weights with any specific DMD. The neurologists did not do direct comparisons between DMDs. FindingsThe highest overall weighted preference value for the RRMS model was for dimethyl fumarate (63) followed closely by cladribine (62). For patients with RRMS and HDA, cladribine had the highest overall weighted preference value (76), followed by alemtuzumab (62) and natalizumab (61). The benefit–risk balance of cladribine in patients with RRMS and specifically patients with RRMS who exhibited HDA characterized by high relapse activity (≥2 relapses in the previous year) was more favorable than the other DMDs included in the model. ImplicationsThe balance of high efficacy and the safety profile makes cladribine an important treatment option to consider, both in patients with RRMS and patients with HDA. Regular, single-country meetings could be organized to explore how differences in cultural values (scores and weights) and updated input data might affect the usefulness of MCDA in different, real-world, dynamic clinical settings.

Development of a Multi Criteria Decision Analysis Model for Pavement Maintenance at the Network Level: Application of the MACBETH Approach

This paper proposes the application of the MACBETH approach for the development of a multi criteria value model to enable the prioritization of maintenance and rehabilitation interventions in the pavements of road networks according to technical, economic, and social aspects. A case-study design considering the Portuguese network was undertaken to illustrate the proposed approach. Using an interactive structuring process developed with the decision-maker, a set of five evaluation criteria related to maintenance costs, traffic, pavement condition, safety, and social equity were defined. The MACBETH approach was then used to build value functions and establish relative weights for the criteria. An application of the resulting model was made to a portfolio of possible intervention projects, prioritizing them according to the principle of benefit/cost maximization. The case-study demonstrated the application of the MACBETH approach and its suitability to solve the decision-making problem, thus proving the usefulness of this approach for road agencies with pavement management responsibilities.

Combining multicriteria decision analysis and network-based model to assess the vulnerability of commercial Cuban poultry to avian influenza viruses

Combining multicriteria decision analysis and network-based model to assess the vulnerability of commercial Cuban poultry to avian influenza viruses

A cyberGIS-enabled multi-criteria spatial decision support system: A case study on flood emergency management

ABSTRACTWith the increased frequency of natural hazards and disasters and consequent losses, it is imperative to develop efficient and timely strategies for emergency response and relief operations. In this paper, we propose a cyberGIS-enabled multi-criteria spatial decision support system for supporting rapid decision making during emergency management. It combines a high-performance computing environment (cyberGIS-Jupyter) and multi-criteria decision analysis models (Weighted Sum Model (WSM) and Technique for Order Preference by Similarity to Ideal Solution Model (TOPSIS)) with various types of social vulnerability indicators to solve decision problems that contain conflicting evaluation criteria in a flood emergency situation. Social media data (e.g. Twitter data) was used as an additional tool to support the decision-making process. Our case study involves two decision goals generated based on a past flood event in the city of Austin, Texas, U.S.A. As our result shows, WSM produces more diverse values and higher output category estimations than the TOPSIS model. Finally, the model was validated using an innovative questionnaire. This cyberGIS- enabled spatial decision support system allows collaborative problem solving and efficient knowledge transformation between decision makers, where different emergency responders can formulate their decision objectives, select relevant evaluation criteria, and perform interactive weighting and sensitivity analyses.

Multi-Criteria Decision Analysis Under Uncertainty: Two Approaches to Incorporating Data Uncertainty into Water, Sanitation and Hygiene Planning

In the era of the Sustainable Development Goals, for which one of the aims is to provide universal access to safe water, sanitation and hygiene (WASH) services, it is crucial to target and prioritize those who remain unserved. Multi-criteria decision analysis (MCDA) models can play an important role in WASH planning by supporting priority-setting and policy-making. However, in order to avoid misleading assumptions and policy decisions, data uncertainty — intrinsic to the available collection methods — must be integrated into the decision analysis process. In this paper, we present two approaches to incorporating data uncertainty into MCDA models (MAUT and ELECTRE-III). We use WASH planning in rural Kenya as a case study to illustrate and compare the two approaches. The comparison focuses on the way these two models handle uncertainty in the available data. The analysis shows that, while both methods incorporate data uncertainty in a considerably different manner, they lead to similar prioritization settings.

Multi-Criteria Decision Analysis (MCDA) Models in Health Technology Assessment of Orphan Drugs-a Systematic Literature Review. Next Steps in Methodology Development?

Background: Multi-criteria decision analysis (MCDA) is a decision-making tool that can take into account multidimensional factors and enables comparison of (medical) technologies by combining individual criteria into one overall appraisal. The MCDA approach has slowly gained traction within Health Technology Assessment (HTA) and its elements are gradually being incorporated into HTA across Europe. Several groups of scientists have proposed MCDA approaches targeted toward orphan drugs and rare diseases by including criteria specific to rare diseases. The goal of this article is to provide an overview of the current state of knowledge and latest developments in the field of MCDA in HTA for orphan drugs, to review existing models, their design characteristics, as well as to identify opportunities for further model improvement.Methods: A systematic literature search was conducted in January 2018 using four databases: MEDLINE (Pubmed), EBSCO HOST, EMBASE, and Web of science to find publications related to use of MCDA in the rare disease field (keywords: MCDA/orphan drug/rare disease and synonyms). Identified MCDA models were analyzed, e.g., structure, criteria, scoring, and weighting methodology.Results: Two hundred and eleven publications were identified, of which 29 were included after removal of duplicates. 9 authors developed own MCDA models, 7 of which based on literature reviews intended to identify the most important and relevant decision criteria in the model. In 13 publications (8 models) weights were assigned to criteria based on stakeholder input. The most commonly chosen criteria for creation of the MCDA models were: comparative effectiveness/efficacy, the need for intervention, and disease severity. Some models have overlapping criteria, especially in the treatment cost and effectiveness areas.Conclusions: A range of MCDA models for HTA have been developed, each with a slightly different approach, focus, and complexity, including several that specifically target rare diseases and orphan drug appraisal. Models have slowly progressed over the years based on pilots, stakeholder input, sharing experiences and scientific publications. However, full consensus on model structure, criteria selection and weighting is still lacking. A simplification of the MCDA model approach may increase its acceptance. A multi-stakeholder discussion on fundamental design and implementation strategies for MCDA models would be beneficial to this end.

PCN307 - APPLYING MULTI-CRITERIA DECISION ANALYSIS (MCDA) AS AN EVIDENCE BASED HEALTH TECHNOLOGY ASSESSMENT IN HEALTH CARE DECISION MAKING IN EGYPT

To move from a “lowest price” policy to a value-based price evaluation through multi-criteria decision analysis (MCDA), decisions makers, especially those in emerging countries, need to understand how to identify and prioritize the criteria that matters most in their population. The objective of the study is to raise the awareness of decision makers on using MCDA in health care decision making in Egypt and to do a real application on how to develop a rating tool to assess mesothelioma interventions; pemetrexed and gemcitabine. An MCDA framework was developed using multiple criteria for the evaluation of pemetrexed and gemcitabine, using a numerical scoring system on a scale of 1 to 3 for each criterion. A Delphi study was then performed to discuss a final list of criteria with clear definitions and potential scoring scales. An appraisal group was convened and participants were asked to express their individual perspectives, by assigning weights to each criterion of the MCDA model. Participants then assigned performance scores for each medicine using available evidence and real-world evidence. MCDA estimates of perceived value were calculated by combining normalized weights and scores. This study resulted in a preliminary rating tool for assessing mesothelioma interventions in Egypt. The tool consists of 11 criteria covering context of drugs, outcomes of drugs, economics and quality of evidence, with clear definitions and potential scoring scales. By calculating the total aggregate score using average weight and average score assigned to each pemetrexed and gemcitabine on each decision criteria of the MCDA model. Pemetrexed got the highest total aggregate score equal to 1.07 compared to gemcitabine 0.99 of the MCDA models. This study describes the development of a rating tool to assess mesothelioma interventions in Egypt. This tool may contribute to a more transparent priority-setting process and fairer decision-making.

Assessment of and Improvement Strategies for the Housing of Healthy Elderly: Improving Quality of Life

Global population is aging, particularly in developed countries and cities. Through the “Friendly Elderly Cities” and “Aging in Place” initiatives, various countries are making great efforts to improve the welfare and quality of life of the elderly, with the aim of catering to the physical and spiritual health as well as social welfare of the elderly. In this regard, the improvement of the housing environment of the elderly is one of the key factors in their quality of life and health. This study aims to effectively assess and improve the housing environment of the elderly in order to enhance their quality of life; it also aims to contribute the knowledge about improving elderly housing by applying an assessment framework using expert interviews and data collected from relevant literature. Using a mixed Multi-Criteria Decision Analysis model, a combination of the DEMATEL (Decision Making Trial and Evaluation Laboratory)-based ANP (Analytic Network Process) (DANP) and the modified VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods, and the investigations and assessments in the case study, this study proposes strategies and directions for improving elderly housing. The study results indicate that, as distinct from using traditional methodologies where the hypothesis criteria are mutually independent, the proposed hybrid model (examining real-life problems by considering the mutual influences of factors) identifies a priority sequence wherein emphasis is placed on improving ventilation and air quality rather than adjustment of temperature. The systemic way of thinking shifts the focus from the most apparent problems to the root cause of the problems. Doing so avoids any mismatch of resource allocation in decision-making and thus maximizes the efficiency and sustainability of the improvement.

Application of fuzzy logic in the preparation of hazard maps of landslides triggered by the twin Ahar-Varzeghan earthquakes (2012)

The twin Ahar-Varzeghan earthquakes (Mw = 6.4 and Mw 6.2, August 12, 2012) are among the most severe and destructive seismic events to have occurred in the northwest part of Iran in the last century. The main shock of this event was felt within a 300 km radius in most of the northwest provinces of Iran, including West Azerbaijan, Ardebil, Gilan, Zanjan, Alborz, Hamedan, and Kurdistan, as well as in neighboring countries such as Republic of Azerbaijan, Armenia, and Turkey. The quake caused some large landslides, which led to remarkable economic losses in the region. Landslide susceptibility mapping is one of the useful tools that can be applied in disaster management and planning development activities in mountainous areas. In this research, a geographic information system (GIS)-based multi-criteria decision analysis model (fuzzy logic) is used to evaluate landslide susceptibility within the area stricken by the twin Ahar-Varzeghan earthquakes (2012). Thus, a rigorous field-based investigation was conducted during several days of fieldwork to prepare a database of landslides triggered in the earthquake-stricken area. The extended fieldwork was carried out to scrutinize the basic map of the slope instabilities plotted immediately after the earthquake. During the fieldwork, 47 fall and topple zones, nine soil slides, 13 rock slides, two areas of lateral spreading, and one rapid soil flow were detected. The ground strength class, slope angle, normalized difference moisture index (NDMI), normalized difference vegetation index (NDVI), distance from the rivers and roads, and shake intensity were selected as the input layers for fuzzy logic analysis in a GIS environment. Next, the performance of various fuzzy operators in landslide susceptibility mapping was empirically compared by applying fuzzy operators [intersection (AND), union (OR), algebraic sum (SUM), multiplication (PRODUCT)] and different fuzzy gamma values of fuzzy overlay. The results showed that the majority of the landslides fall in the “high” and “very high” susceptibility classes. We found that there is a satisfactory consistency between the landslide susceptibility map prepared using the fuzzy union (OR) operator and the landslide distribution map.

How to watch the watchmen? The role and measurement of regulatory governance

This article proposes a method to evaluate regulatory governance. For this purpose, we develop a novel multicriteria decision analysis modeling approach. Performance criteria were organized into key dimensions and descriptors were constructed for each of the criteria. Reference regulator profiles were developed for scoring and comparison purposes. The subjective judgment of experts was incorporated in the modeling process. The “Regulatory Governance Scorecard” is calculated for the Portuguese regulatory agencies to illustrate the model outputs and demonstrate its potential usefulness.

Decision Support for the Comparative Evaluation and Selection of Analytical Methods: Detection of Genetically Modified Organisms as an Example

The selection of the best-fit-for-purpose analytical method to be implemented in the laboratory is difficult due to availability of multiple methods, targets, aims of detection, and different kinds and sources of more or less reliable information. Several factors, such as method performance, practicability, cost of setup, and running costs need to be considered together with personnel training when selecting the most appropriate method. The aim of our work was to prepare a flexible multicriteria decision analysis model suitable for evaluation and comparison of analytical methods used for the purpose of detecting and/or quantifying genetically modified organisms, and to use this model to evaluate a variety of changing analytical methods. Our study included selection of PCR-, isothermal-, protein-, microarray-, and next-generation sequencing-based methods in simplex and/or multiplex formats. We show that the overall result of their fitness for purpose is relatively similar; however, individual criteria or a group of related criteria exposed more substantial differences between the methods. The proposed model of this decision support system enables easy modifications and is thus suitable for any other application of complex analytical methods.

Comparing Machine Learning and Decision Making Approaches to Forecast Long Lead Monthly Rainfall: The City of Vancouver, Canada

Estimating maximum possible rainfall is of great value for flood prediction and protection, particularly for regions, such as Canada, where urban and fluvial floods from extreme rainfalls have been known to be a major concern. In this study, a methodology is proposed to forecast real-time rainfall (with one month lead time) using different number of spatial inputs with different orders of lags. For this purpose, two types of models are used. The first one is a machine learning data driven-based model, which uses a set of hydrologic variables as inputs, and the second one is an empirical-statistical model that employs the multi-criteria decision analysis method for rainfall forecasting. The data driven model is built based on Artificial Neural Networks (ANNs), while the developed multi-criteria decision analysis model uses Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. A comprehensive set of spatially varying climate variables, including geopotential height, sea surface temperature, sea level pressure, humidity, temperature and pressure with different orders of lags is collected to form input vectors for the forecast models. Then, a feature selection method is employed to identify the most appropriate predictors. Two sets of results from the developed models, i.e., maximum daily rainfall in each month (RMAX) and cumulative value of rainfall for each month (RCU), are considered as the target variables for forecast purpose. The results from both modeling approaches are compared using a number of evaluation criteria such as Nash-Sutcliffe Efficiency (NSE). The proposed models are applied for rainfall forecasting for a coastal area in Western Canada: Vancouver, British Columbia. Results indicate although data driven models such as ANNs work well for the simulation purpose, developed TOPSIS model considerably outperforms ANNs for the rainfall forecasting. ANNs show acceptable simulation performance during the calibration period (NSE up to 0.9) but they fail for the validation (NSE of 0.2) and forecasting (negative NSE). The TOPSIS method delivers better rainfall forecasting performance with the NSE of about 0.7. Moreover, the number of predictors that are used in the TOPSIS model are significantly less than those required by the ANNs to show an acceptable performance (7 against 47 for forecasting RCU and 6 against 32 for forecasting RMAX). Reliable and precise rainfall forecasting, with adequate lead time, benefits enhanced flood warning and decision making to reduce potential flood damages.

Multicriteria decision analysis (MCDA) for health technology assessment: the Queensland Health experience.

Objectives In determining whether new health technologies should be funded, health technology assessment (HTA) committees prefer explicit to implicit methods of analysis in enhancing transparency and consistency of decision making. The aim of this study was to develop and pilot a multicriteria decision analysis (MCDA) framework for the Queensland Department of Health HTA program committee, which weighted decision making criteria according to their perceived importance as determined by group consensus. Methods The criteria used in the MCDA framework were identified by reviewing the five unweighted criteria used in the existing process, consultation with committee members and literature review. Criteria were clearly defined and ordinal categories of lowest to highest preferred were assigned against which technology submissions would be rated. Criteria weights were determined through a discrete choice experiment (DCE) survey of committee members using validated software. Mean weighted technology scores were then used to guide deliberative discussions in determining final funding decisions. Results The MCDA framework created one additional criterion to the previous five. The criteria and their mean weights identified through the DCE survey were clinical benefit and safety (27.2%), quality of evidence (19.2%), implementation capacity (16.9%), innovation (15.4%), burden of disease and clinical need (13.3%) and societal and ethical values (8.0%). Criterion weights varied considerably between individual committee members, with one criterion having a difference of 36.9% between the highest and lowest preference weights. Following deliberative discussions, all but one of 10 submissions were awarded funding. The submission not supported received the third lowest score through the MCDA model. Conclusions This pilot application of an MCDA framework, as a complement to committee deliberation, conferred greater transparency and objectivity on HTA assessment of technologies. The framework converted an implicit, unweighted review process to one that is more explicit, flexible in weighting importance and pragmatic. What is known about the topic? HTA programs involve complex decision-making processes requiring the consideration of multiple criteria. Explicit methods of analysis that use weighted criteria according to their relative importance enhance transparency and consistency of decision making by HTA committees, and are preferred to implicit reviews using unweighted criteria. What does this paper add? This article describes the development and piloting of an MCDA framework that aims to improve transparency, objectivity and consistency of funding decisions of the Queensland HTA committee. Criteria were identified through a review of current processes, committee discussions and a literature review, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) quality of evidence system. Criteria were weighted using a discrete choice experiment involving committee members. Using weighted criteria, mean technology scores were calculated and incorporated into deliberative discussions to determine funding decisions. What are the implications for practitioners? The MCDA framework described here converted a more implicit, unweighted process to one that was more pragmatic, explicit and flexible in scoring HTA submissions. This framework may be useful to other HTA programs and could be expanded to resource allocation decision making in many other healthcare settings.

Combining the PROMETHEE method and mathematical programming for multi-objective facility location problem

The decision making concerning where to locate new facilities is crucial in designing distribution networks. In the present study, the supply chain concept and the multi-objective facility location problem were studied. We integrated also multi-criteria decision analysis (MCDA) and linear programming model (LPM) methods to support selection of best sites according many objectives. In addition, facility location decision involves both qualitative and quantitative criteria. In order to select the best location, it is often necessary to compromise possibly conflicting tangible and intangible factors. The major weaknesses of the different multi-objective programming models used to solve the problem are that they are basically mathematical and they ignore qualitative objectives, such as the environmental and socio-economic factors which are important in facility location selection. In this paper, we reported the obtained results when applying an integrated MCDA/LPM approach to solve the facility location problem (FLP) for an aluminium company in Tunisia. We combined three methods, preference-ranking organisation method for enrichment evaluation (PROMETHEE) and a linear programming model (LPM), using the stretching and shrinking graphs method (SSG method).

Aconitum in treatment of rheumatoid arthritis: benefit-risk assessment

Rheumatoid arthritis (RA) has the characteristics of long course of disease and difficulty in treatment. The conventional therapy may easily induce adverse drug reactions or events (ADR/ADE) due to the long-time medication. Thus, it should be given special attentions to treatment benefit and medication risk of RA patients. Aconitum, a kind of toxic traditional Chinese herbs, is an important complement therapy for RA, with some controversy in clinical application. Coming straight to the practical problem of combined use of traditional Chinese medicines (TCM) and Western medicines (WM), this study conducted quantitative assessment on the benefits and risks of Aconitum using combined with WM or not, which was carried out by the method of multi-criteria decision analysis model. RevMan 5.2 software was used to separately analyze the results of every index of 21 random clinical trials (RCTs) of Aconitum exclusive use in the treatment of RA, and 49 RCTs of Aconitum combined use with WM. The merged results indicated that as compared with the conventional therapy of WM, no matter the exclusive use or the combined use of Aconitum could improve the efficacy and decrease the incidence of ADR/ADEs. Based on the benefit-risk assessment decision tree of RA treatment, Hiview 3 software and Crystal Ball Monte Carlo simulation were used to calculate the benefit value, risk value and benefit-risk value of Aconitum exclusive use and the combined use of Aconitum with WM. The results showed that the combination therapy had significantly better benefits than Aconitum exclusive using, difference value was 15, (95%CI[9.72, 20.25]), but the risk of combined use was higher difference value=23, （95%CI[15.57, 30.55]）. In comprehensive consideration of the benefit and risk, the total benefit-risk value of using Aconitum alone was 58, while that of the combination therapy was 55, and the probability of the former superior to the latter was 81.07%. The study showed that Aconitum was the important therapy to supply RA treatment. In clinical application, the patient's acceptability of benefit and risk need to be considered; if patients cannot bear the risk, the combination use of Aconitum and WM is not recommended.

Method Development for Clinical Comprehensive Evaluation of Pediatric Drugs Based on Multi-Criteria Decision Analysis: Application to Inhaled Corticosteroids for Children with Asthma.

Establishing a comprehensive clinical evaluation system is critical in enacting national drug policy and promoting rational drug use. In China, the 'Clinical Comprehensive Evaluation System for Pediatric Drugs' (CCES-P) project, which aims to compare drugs based on clinical efficacy and cost effectiveness to help decision makers, was recently proposed; therefore, a systematic and objective method is required to guide the process. An evidence-based multi-criteria decision analysis model that involved an analytic hierarchy process (AHP) was developed, consisting of nine steps: (1) select the drugs to be reviewed; (2) establish the evaluation criterion system; (3) determine the criterion weight based on the AHP; (4) construct the evidence body for each drug under evaluation; (5) select comparative measures and calculate the original utility score; (6) place a common utility scale and calculate the standardized utility score; (7) calculate the comprehensive utility score; (8) rank the drugs; and (9) perform a sensitivity analysis. The model was applied to the evaluation of three different inhaled corticosteroids (ICSs) used for asthma management in children (a total of 16 drugs with different dosage forms and strengths or different manufacturers). By applying the drug analysis model, the 16 ICSs under review were successfully scored and evaluated. Budesonide suspension for inhalation (drug ID number: 7) ranked the highest, with comprehensive utility score of 80.23, followed by fluticasone propionate inhaled aerosol (drug ID number: 16), with a score of 79.59, and budesonide inhalation powder (drug ID number: 6), with a score of 78.98. In the sensitivity analysis, the ranking of the top five and lowest five drugs remains unchanged, suggesting this model is generally robust. An evidence-based drug evaluation model based on AHP was successfully developed. The model incorporates sufficient utility and flexibility for aiding the decision-making process, and can be a useful tool for the CCES-P.

Use of multi-criteria decision analysis to identify potentially dangerous glacial lakes

Glacial Lake Outburst Floods (GLOFs) represent a significant threat in deglaciating environments, necessitating the development of GLOF hazard and risk assessment procedures. Here, we outline a Multi-Criteria Decision Analysis (MCDA) approach that can be used to rapidly identify potentially dangerous lakes in regions without existing tailored GLOF risk assessments, where a range of glacial lake types exist, and where field data are sparse or non-existent. Our MCDA model (1) is desk-based and uses freely and widely available data inputs and software, and (2) allows the relative risk posed by a range of glacial lake types to be assessed simultaneously within any region. A review of the factors that influence GLOF risk, combined with the strict rules of criteria selection inherent to MCDA, has allowed us to identify 13 exhaustive, non-redundant, and consistent risk criteria. We use our MCDA model to assess the risk of 16 extant glacial lakes and 6 lakes that have already generated GLOFs, and found that our results agree well with previous studies. For the first time in GLOF risk assessment, we employed sensitivity analyses to test the strength of our model results and assumptions, and to identify lakes that are sensitive to the criteria and risk thresholds used. A key benefit of the MCDA method is that sensitivity analyses are readily undertaken. Overall, these sensitivity analyses lend support to our model, although we suggest that further work is required to determine the relative importance of assessment criteria, and the thresholds that determine the level of risk for each criterion. As a case study, the tested method was then applied to 25 potentially dangerous lakes in the Bolivian Andes, where GLOF risk is poorly understood; 3 lakes are found to pose ‘medium’ or ‘high’ risk, and require further detailed investigation.

Combining user preferences and expert opinions: a criteria synergy-based model for decision making on the Web

Customers strongly base their e-commerce decisions on the opinions of others by checking reviews and ratings provided by other users. These assessments are overall opinions about the product or service, and it is not possible to establish why they perceive it as good or bad. To understand this “why”, it is necessary an expert’s analysis concerning the relevant factors of the product or service. Frequently, these two visions are not coincident and the best product for experts may not be the best one for users. For this reason, trustworthy decision-making methods that integrate the mentioned views are highly desirable. This article proposes a multi-criteria decision analysis model based on the integration of users’ preferences and experts’ opinions. It combines the majority’s opinion and criteria synergy to provide a unified perspective in order to support consumers’ ranking-based decisions in social media environments. At the same time, the model supplies useful information for managers about strengths and weaknesses of their product or service according to users’ experience and experts’ judgment. The aggregation processes and synergy criteria are modeled in order to obtain an adequate consensus mechanism. Finally, in order to test the proposed model, several simulations using hotel valuations are performed.