The Group Decision-Making Using Pythagorean Fuzzy Entropy and the Complex Proportional Assessment

The Pythagorean fuzzy sets conveniently capture unreliable, ambiguous, and uncertain information, especially in problems involving multiple and opposing criteria. Pythagorean fuzzy sets are one of the popular generalizations of the intuitionistic fuzzy sets. They are instrumental in expressing and managing hesitant under uncertain environments, so they have been involved extensively in a diversity of scientific fields. This paper proposes a new Pythagorean entropy for Multi-Criteria Decision-Analysis (MCDA) problems. The entropy measures the fuzziness of two fuzzy sets and has an influential position in fuzzy functions. The more comprehensive the entropy, the more inadequate the ambiguity, so the decision-making established on entropy is beneficial. The COmplex PRoportional ASsessment (COPRAS) method is used to tackle uncertainty issues in MCDA and considers the singularity of one alternative over the rest of them. This can be enforced to maximize and minimize relevant criteria in an assessment where multiple opposing criteria are considered. Using the Pythagorean sets, we represent a decisional problem solution by using the COPRAS approach and the new Entropy measure.

Two Sides of the Same Coin? A Dual Multiple Criteria Decision Analysis of Novel Treatments Against Rheumatoid Arthritis in Physicians and Patients

PURPOSE

Available treatment options for rheumatoid arthritis (RA) differ in important aspects. In this sense, each RA treatment option is accompanied by a spectrum of characteristics that collectively constitute its comprehensive "value," as viewed from the physician's or the patient's perspective. The objective of this study was to perform a multiple criteria decision analysis of different RA treatments from the perspective of physicians and patients and to outline the respective aspects of value for each treatment METHODS: A literature review was performed for constructing a set of criteria (N = 8) for the multiple criteria decision analysis. Workshops for the elicitation of preferences occurred separately for physicians and patients. A performance matrix was populated via 2 network meta-analyses plus converged clinical opinion. Criteria were hierarchically classified by application of pairwise comparisons, and criteria weights were attributed by point allocation through convergence of opinions. Performances in both panels were scored by using a 100-point scale. A linear additive value function was used for the calculation of total value estimates.

FINDINGS

Both panels provided their consensus. The hierarchical classification of attributes from the physician perspective placed the highest values on the criteria of severe adverse events, clinical efficacy, route of administration, and cost per year for the third-party payer. From the patient perspective, the highest ranking criteria were clinical efficacy, severe adverse events, percentage of patients remaining with the same targeted immune modulator for 1 year ("drug survival"), and cost per year for the third-party payer.

IMPLICATIONS

In an era of multiple options and varying preferences, RA treatments must be evaluated by taking into consideration patients' preferences as well, as to cover the full spectrum of value elements rather than simply clinical outcomes. The results of this analysis show that physicians and patients share similarities but also marked differences in terms of the aspects of treatment that they perceive as more valuable.

Value-based decision-making for orphan drugs with multiple criteria decision analysis: burosumab for the treatment of X-linked hypophosphatemia

OBJECTIVE

Burosumab is an orphan medicinal product (OMP) approved in Europe for the treatment of X-linked hypophosphatemia (XLH). The aim of this study was to assess the value of burosumab versus conventional therapy for the treatment of paediatric XLH, through a multi-criteria decision analysis (MCDA) framework for health technology assessment (HTA) of OMPs in Portugal.

METHODS

The MCDA framework considered 14 criteria related to disease burden, therapeutic value and economic burden. A multidisciplinary panel of national stakeholders participated in a two-phase exercise. In the first phase, relative weights and part-worth utilities for the criteria and their levels were elicited and a reimbursement likelihood function was calibrated through adaptive conjoint analysis. In the second phase, burosumab and conventional therapy were assessed against the criteria, providing a global value score (0-100) and reimbursement likelihood (0-100%) for both.

RESULTS

Of the 14 criteria, disease burden, therapeutic value and economic burden criteria represented 27.29%, 57.17% and 15.53% of the total weight in the decision, respectively. All disease burden and some therapeutic value criteria, typically not included in traditional HTA, represented 47.88% of the total weight. Burosumab was unanimously considered superior to conventional therapy, with an average (range) global value score of 84.96 (82.48-86.54) against 48.06 (43.37-57.68), and reimbursement likelihood of 97.50% (96.78%-98.32%) against 43.66% (31.48%-68.73%), respectively.

CONCLUSIONS

MCDA represents a powerful tool in HTA decision-making for OMPs. The results of this MCDA acknowledge burosumab as a disease-modifying drug, deemed superior to conventional therapy for the treatment of paediatric XLH.

Developing composite indices of geographical access and need for nursing home care in Ireland using multiple criteria decision analysis

Background: Spatial accessibility has consistently been shown to influence utilisation of care and health outcomes, compared against local population needs. We sought to identify how appropriately nursing homes (NHs) are distributed in Ireland, as its NH market lacks central planning. Methods: We used multiple criteria decision analysis (MCDA) approaches to develop composite indices of both access (incorporating measures of availability, choice, quality and affordability) and local NH need for over 65s (relating to the proportion living alone, with cognitive disabilities or with low self-rated health, estimated scores for activities of daily living and instrumental activities of daily living, the average number of disabilities per person and the average age of this group). Data for need were derived from census data. Results were mapped to better understand underlying geographical patterns. Results: By comparing local accessibility and need, underserved areas could be identified, which were clustered particularly in the country's northwest. Suburbs, particularly around Dublin, were by this measure relatively overserved. Conclusions: We have developed multi-dimensional indices of both accessibility to, and need for, nursing home care. This was carried out by combining granular, open data sources and elicited expert/stakeholder opinion from practitioners. Mapping these data helped to highlight clear evidence of inequitable variation in nursing home distribution.

An Analytical Mobile App for Shared Decision Making About Prenatal Screening: Protocol for a Mixed Methods Study

BACKGROUND

Decisions about prenatal screening to assess the risk of genetic conditions such as Down syndrome are complex and should be well informed. Moreover, the number of available tests is increasing. Shared decision making (SDM) about testing could be facilitated by decision aids powered by mobile technology.

OBJECTIVE

In this mixed methods study, we aim to (1) assess women's needs and preferences regarding using an app for considering prenatal screening, (2) develop a decision model using the analytical hierarchy process, and (3) develop an analytical app and assess its usability and usefulness.

METHODS

In phase 1, we will assess the needs of 90 pregnant women and their partners (if available). We will identify eligible participants in 3 clinical sites (a midwife-led birthing center, a family practice clinic, and an obstetrician-led hospital-based clinic) in Quebec City and Montreal, Canada. Using semistructured interviews, we will assess participants' attitudes toward mobile apps for decision making about health, their current use of apps for health purposes, and their expectations of an app for prenatal testing decisions. Self-administered questionnaires will collect sociodemographic information, intentions to use an app for prenatal testing, and perceived importance of decision criteria. Qualitative data will be transcribed verbatim and analyzed thematically. Quantitative data will be analyzed using descriptive statistics and the analytic hierarchy process (AHP) method. In phase 2, we will develop a decision model using the AHP whereby users can assign relative importance to criteria when deciding between options. We will validate the model with potential users and a multidisciplinary team of patients, family physicians, primary care researchers, decision sciences experts, engineers, and experts in SDM, genetics, and bioethics. In phase 3, we will develop a prototype of the app using the results of the first 2 phases, pilot test its usefulness and usability among a sample of 15 pregnant women and their partners (if available), and improve it through 3 iterations. Data will be collected with a self-administered questionnaire. Results will be analyzed using descriptive statistics.

RESULTS

Recruitment for phase 1 will begin in 2019. We expect results to be available in 2021.

CONCLUSIONS

This study will result in a validated analytical app that will provide pregnant women and their partners with up-to-date information about prenatal screening options and their risks and benefits. It will help them clarify their values and enable them to weigh the options to make informed choices consistent with their preferences and values before meeting face-to-face with their health care professional. The app will be easy to update with the latest information and will provide women with a user-friendly experience using their smartphones or tablets. This study and the resulting app will contribute to high-quality SDM between pregnant women and their health care team.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/13321.

Multiple Criteria Decision Analysis for Health Care Decision Making--Emerging Good Practices: Report 2 of the ISPOR MCDA Emerging Good Practices Task Force

Health care decisions are complex and involve confronting trade-offs between multiple, often conflicting objectives. Using structured, explicit approaches to decisions involving multiple criteria can improve the quality of decision making. A set of techniques, known under the collective heading, multiple criteria decision analysis (MCDA), are useful for this purpose. In 2014, ISPOR established an Emerging Good Practices Task Force. The task force's first report defined MCDA, provided examples of its use in health care, described the key steps, and provided an overview of the principal methods of MCDA. This second task force report provides emerging good-practice guidance on the implementation of MCDA to support health care decisions. The report includes: a checklist to support the design, implementation and review of an MCDA; guidance to support the implementation of the checklist; the order in which the steps should be implemented; illustrates how to incorporate budget constraints into an MCDA; provides an overview of the skills and resources, including available software, required to implement MCDA; and future research directions.

Multiple Criteria Decision Analysis for Health Care Decision Making--An Introduction: Report 1 of the ISPOR MCDA Emerging Good Practices Task Force

Health care decisions are complex and involve confronting trade-offs between multiple, often conflicting, objectives. Using structured, explicit approaches to decisions involving multiple criteria can improve the quality of decision making and a set of techniques, known under the collective heading multiple criteria decision analysis (MCDA), are useful for this purpose. MCDA methods are widely used in other sectors, and recently there has been an increase in health care applications. In 2014, ISPOR established an MCDA Emerging Good Practices Task Force. It was charged with establishing a common definition for MCDA in health care decision making and developing good practice guidelines for conducting MCDA to aid health care decision making. This initial ISPOR MCDA task force report provides an introduction to MCDA - it defines MCDA; provides examples of its use in different kinds of decision making in health care (including benefit risk analysis, health technology assessment, resource allocation, portfolio decision analysis, shared patient clinician decision making and prioritizing patients' access to services); provides an overview of the principal methods of MCDA; and describes the key steps involved. Upon reviewing this report, readers should have a solid overview of MCDA methods and their potential for supporting health care decision making.