An Empirical Study of Value Evaluation by Multi-criteria Decision Analysis for Orphan Medicinal Product

An Empirical Study of Value Evaluation by Multi-criteria Decision Analysis for Orphan Medicinal Product | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Empirical Study of Value Evaluation by Multi-criteria Decision Analysis for Orphan Medicinal Product Xian Tang, Handong Chen, Yuliang Xiang, Ming Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3724723/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To conduct an empirical study on the comprehensive value of specific orphan drugs based on the constructed Multi-criteria decision analysis (MCDA) framework, evaluate the feasibility of applying MCDA to evaluate value of orphan drugs, and provide reference for expanding the decision-making ideas and evidence of medical insurance access for orphan drugs. Methods Dimethyl fumarate, Laronidase, and Emicizumab were selected as empirical drugs, and collected their empirical data by literature retrieval, third-party data extraction and enterprise consultation. The empirical drugs were scored independently by the research team and stakeholder group based on the constructed MCDA framework and weight, also combined with qualitative evaluation and finally analyzed the results of the MCDA empirical evaluation. Results In the quantitative criteria evaluation, the calculated MCDA scores of the three drugs were 0.50, 0.57 and 0.52 respectively by the research team, and 0.47, 0.59 and 0.51 by the stakeholder group, which shows the high scoring consistency of two groups, and the obtained value priority of the quantitative criteria of the three drugs from high to low is Laronidase, Emicizumab, and Dimethyl fumarate. In the qualitative criteria evaluation, the evaluation results of the two groups did not achieve statistically significant consistency, but for the criteria “Mandate and scope of healthcare system” and “Affordability of medical insurance fund”, they scored the same. Conclusions The empirical study proves that MCDA is feasible in the evaluation of orphan drug value in China, and can be used as a supplementary tool for the access decision of medical insurance drugs. Figures Figure 1 Figure 2 Figure 3 Key points for decision makers For rare diseases, it is difficult to use standard HTA to assess the value of orphan medicinal products due to the lack of appropriate diagnostic methods and scarcity of health professionals, as well as insufficient evidence of clinical efficacy and disease burden caused by small patient populations. MCDA is one of the comprehensive evaluation methods of drug value, which is a systematic decision-making thinking and method that integrates decision-making concept and practice. At present, MCDA has been more and more applied to drug procurement by bidding, drug selection in essential drugs list and medical insurance drug reimbursement list, as well as the evaluation of clinical treatment methods and the selection of medical insurance programs. This empirical research tests shown that MCDA framework had good operability and feasibility, and it could be used as supplementary evidence in the real evaluation of the value for OMP. 1 Introduction Evidence-based economic evaluation is the basis of value assessment in many countries when making reimbursement decisions. However, for rare diseases, it is difficult to use standard health technology assessments (HTA) to assess the value of orphan medicinal product (OMP) due to insufficient evidence of clinical efficacy and disease burden caused by small patient populations[1-3]. Therefore, it is necessary to adopt a more comprehensive method to evaluate the value of OMP from multiple criteria. Multi-criteria decision analysis (MCDA) is one of the comprehensive evaluation methods of drug value, which is a systematic decision-making thinking and method that integrates decision-making concept and practice. At present, MCDA has been more and more applied to drug procurement by bidding, drug selection in essential drugs list and medical insurance drug reimbursement list, and other related fields. Internationally, MCDA is increasingly advocated as a method to evaluate the value of OMP[4-6]. Iskrov G et al. (2014)[7] proposed the use of MCDA in the field of OMP, and found that MCDA could improve the fairness and strictness of orphan drug reimbursement decisions. Many studies have also found that MCDA can overcome the above defects in the current HTA for OMP, and can be used by health insurance decision-makers[8-10]. Gilabert-Perramon et al. (2017)[11] constructed a Catalan value evaluation framework for OMP based on the EVIDEM framework in MCDA, and the results showed that MCDA (EVIDEM framework) was helpful and could improve the understanding and transparency for decision-making. Vandewalle B et al. (2021)[12] evaluated the value of Burosumab in the treatment of X-linked hypophosphataemia based on MCDA, and found that MCDA could consider the drug value criteria that not included in the traditional economic evaluation, and the expert consistency was high. In the actual implementation of national medical insurance access evaluation in China, although priority is given to OMPs, the access procedures and evaluation methods for OMP are consistent with those for conventional drugs. That is, the cost-effectiveness analysis results in economic evaluation are compared with the threshold determined based on GDP, and the budget impact analysis results are also considered. Combined with the current status of medical insurance affordability, drugs suitable for inclusion in medical insurance reimbursement are finally determined through negotiation of price reduction[13]. For innovative orphan drugs, how to comprehensively balance the value, accessibility, affordability, and build a multi-criteria value evaluation framework suitable for medical insurance access is a hot topic in recent years. Yuan et al. (2021) constructed the evaluation index system of medical insurance access for OMP in China through literature research and expert consultation[13]. The study found that the medical insurance access evaluation index of OMP should be multi-criteria and focused on the treatment demand, actual treatment effect, social impact and equity on the basis of considering the cost-effectiveness. Based on the EVIDEM framework in MCDA, Zhang et al. (2022) organized experts in related fields to formulate the Expert Consensus on the Application of Multi-criteria Decision Analysis to the Clinical Comprehensive Evaluation of orphan medical product (2022), which provided normative methodological guidance for the clinical comprehensive evaluation of OMP[14] . However, there is no empirical study and framework test on the constructed framework in Chinese studies, lacking of application examples. In addition, most of the current empirical studies in China and abroad are comparative studies of different drugs treating the same rare disease, and there are few studies on how to compare the value of innovative drugs for treating different rare diseases. This study intends to conduct an empirical study on the value evaluation of three specific orphan drugs that have been marketed and indicated in China, by using the constructed MCDA based valuation framework for OMP. The selected drugs are Dimethyl fumarate for multiple sclerosis, Laronidase for mucopanosaccharidosis, and Emicizumab for hemophilia. Through the empirical data collection and scoring based on the value evaluation framework for OMP, the feasibility of the constructed MCDA criterion framework was tested, and the scoring results of the three drugs were compared. 2 Method 2.1 Construct and assign the weight to the MCDA criterion framework for OMP In this study, the criteria framework of drug value evaluation for OMP that suitable for China based on MCDA was first constructed. We obtained the initial version of the framework based on the criteria of EVIDEM (V.10) framework, by doing some localized translation, and also taking the current Chinese medical insurance policy, characteristics of OMP, and ethical of each criterion into consideration. Then, 15 experts in related fields were invited as members of the stakeholder expert panel to discuss and give advice to the preliminary framework through brainstorming method. After revision, the final framework of OMP value evaluation criteria based on the opinions of stakeholders and operational conditions was formed (Appendix Table 1 in Supplemental Materials). Then, stakeholder experts assigned the weight to each quantitative criterion in the constructed criteria framework by the two-step percentile distribution method. The weighting results were shown in Appendix Figure 1 in Supplemental Materials, and more process and method details were shown in ours another analysis article. 2.2 Empirical study 2.2.1 Empirical drug selection Multiple sclerosis (MS) is an incurable, chronically progressive autoimmune disease that occurs in the central nervous system and was included in the first Rare Diseases Catalogue (2018) in China. At present, several drugs for MS have been marketed in China, such as interferon beta-1a/1b, Fingolimod, Teriflunomide, Dimethyl fumarate, Fampridine, Siponimod, etc. Among them, Dimethyl fumarate (DMF) and Fingolimod, among the oral small molecule drugs, are widely used in clinical application[15], and Fingolimod was approved for marketing in China in 2020 for the treatment of relapse multiple sclerosis (RMS) patients aged 10 years or older, and was included in National Reimbursement Drug List (NRDL) in 2020. DMF was approved for market in China in April 2021, and also can be used clinically for the treatment of adult patients with RMS, and has not been included in the NRDL in China by 2022. Mucopolysaccharidosis (MPS) was divided into 11 subtypes and was included in the Catalogue in China. The main treatment methods for MPS-Ⅰ are hematopoietic stem cell transplantation and enzyme replacement therapy (ERT)[16]. Laronidase is the world's first and only approved ERT for MPS-I, which was approved and marketed in China in June 2020, and it is the only specific therapeutic drug for the treatment of MPS-I in China. It can be used for MPS-I patients of all ages and different disease severity, filling the gap of specific therapeutic drugs and the unmet clinical demand, and it has not been included in the NRDL in China. Haemophilia is an inherited hemorrhagic disease characterized by disorder in the production of coagulation factor Ⅷ (FⅧ), in which haemophilia type A accounts for 80%~85% of all hemophilia. At present, the listed hemophilia drugs in China include Emicizumab, Recombinant Human Coagulation Factor Ⅷ/Ⅶ for Injection, Human Coagulation Factor Ⅷ, and Human Prothrombin Complex. Only Emicizumab is the monoclonal drug, which was approved in November 2018 in China. Its efficacy is better than that of other drugs, but it has not been included in the NRDL in China. Therefore, DMF, Laronidase and Emicizumab was chosen to do the empirical study, as they all treat rare diseases, have certain advantages in the treatment of corresponding diseases, and are expected to be included in NRDL in China. 2.2.2 Empirical data collection Empirical data included information about the treated diseases (disease severity and unmet need), clinical effects and economic outcomes of the drugs, quality of evidence, clinical guidelines, government objectives and policy priorities, and affordability of medical insurance funds. Data sources and search terms were shown in Appendix Table 2 in Supplemental Materials, and specific collection details are shown below. Disease information: The indications of rare diseases of the corresponding drugs were used as search terms, and relevant clinical research and disease review literature was collected. Also some disease data collected from the sample hospital (West China Hospital). Clinical effects of the drug: The name of the empirical drugs and their control drugs were used as search terms. Relevant clinical studies and systematic reviews were collected, combined with clinical effect data provided by drug manufacturers and sample hospital. Economic results: The name of the empirical drugs and their control drugs, and pharmacoeconomics or market research were used as search terms, to collect drug related cost data in China market. If there is no relevant study, we will do the treatment cost estimate. The drug price is from the drug bidding database of yaozhi.com, and the median price of drugs winning the bid in 2021 was selected, combined with the treatment course in the drug instructions, and the treatment cost data from sample hospital, then calculated the treatment cost. Quality of evidence: The research team evaluated the evidence quality of the collected clinical and economic outcomes and summarized the quality, reliability and uncertainty of the available evidence. Clinical Guidelines and Expert Consensus: Using the corresponding indications of rare diseases and clinical guidelines or expert consensus as the keywords, collected the recommendations of the evaluated drugs in the treatment guidelines and expert consensus. Common goals and specific interests: The names of the empirical drugs, their control drugs and the disease were used as keywords, news reports of interest groups related to the rare diseases were collected and summarized. Government objectives and policy priorities: By searching Bailu Thinktank and taking the names of empirical drugs, their control drugs and disease as keywords, the relevant national policies of OMP were collected and summarized. Affordability of medical insurance fund: Since there are no Chinese budget impact analysis (BIA) studies related to the three drugs, this study conducted BIA for them. Drug market data are provided from enterprises, and drug prices are obtained from the database of yaozhi.com and sample hospital. The number of patients with diseases treated by drugs was retrieved from Pubmed, Web of Science, and Chinese Journal Database. Other criteria data: The name of the empirical drugs, their control drugs, disease and other related search terms were used to search. After collecting all the data, the data were summarized into a preliminary empirical data set according to the criteria entry. Then the stakeholder experts discussed the problems existing and the evidence to be supplemented, and put forward opinions and suggestions. After being supplemented and refined based on expert opinions, formed the final empirical data set (Appendix Table 3 in Supplemental Materials). 2.2.3 Empirical performance scoring The empirical data of the three drugs were scored by the research team and the stakeholder experts group respectively, which can help explore the possible problems encountered in the empirical scoring step of the framework, and also compare whether the two teams scored consistently. Then the weight obtained from the stakeholder perspective were used to calculate the final weighted score of the drugs. For the scoring of research team, two members independently scored the performance of each drug on each criterion based on empirical data results. After that, the third member summarized the scoring results and organized discussion, and finally obtained the empirical scores of each criterion for the three drugs. Stakeholder evaluation adopted letter evaluation questionnaire, and invited stakeholder experts to score the performance of the drugs in each criterion based on the empirical data set and their own judgment. In quantitative criteria, scoring the drug's performance in each criterion on a scale of 0 to 5 (non-comparative criteria) or -5 to 5 (comparative criteria). In the qualitative criteria, choosing the most appropriate qualitative evaluation options according to the actual situation of the drug. Microsoft Excel 2019 and SPSS17.0 were used for statistical analysis and calculate the MCDA scores of each drug. The value contribution (Vx) of each quantitative criterion was then calculated as the product of its normalised scoring (Wx, ∑Wx = 1) and standardised score (Sx = score/5). The overall MCDA value estimate (V) of each orphan drugs was calculated based on a linear additive model as a sum the value contributions (Vx) of all (n) criteria of the quantitative criteria. The consistency of the MCDA scores calculated by the research team and the stakeholder expert scoring was compared. The degree of coordination of criterion scores was evaluated by Kendall's coordination coefficient (W). 2.3 Sensitivity analysis The sensitivity analysis was carried out on the quantitative criteria of MCDA framework. The score of the research team and stakeholders in each criterion was added and subdivided by 1 point as the upper and lower limits (the highest limit was 5 points, and the lowest limit was 0 or -5 points), and the sensitivity analysis was carried out on the score value of each criterion. 3 Results 3.1 The constructed value evaluation framework for OMP based on MCDA and the weighting results for the quantitative criteria Based on the stakeholder experts’ opinion, the final OMP value evaluation criteria framework consists 11 quantitative criteria and 8 qualitative criteria, see in the previous article, and Appendix Table 1 in Supplemental Materials. Based on two-step percentile distribution method, the results of the weight of each quantitative criterion were shown in Appendix Figure 1 in Supplemental Materials. The total weight of all criteria was set to 1 and the individual criterion weights were normalized, shown in Figure 1. For this part, more detailed process and analysis were shown in another article of our research. 3.2 MCDA score for quantitative criteria 3.2.1 Scoring results The empirical data scoring results of DMF, Laronidase and Emicizumab by the research team and stakeholders were shown in Table 1. There are some differences between the scoring results from stakeholder and research team, for example, the research team scores have many higher compared with the scores from stakeholders, also with some extreme values, like 5 and -5. For the stakeholder experts, the scoring Kendall's coordination coefficient (W) of DMF, Laronidase and Emicizumab by stakeholders were 0.633, 0.698 and 0.506, respectively, and their progressive significance was 0.00, less than 0.05, indicating that the scoring of these three orphan drugs by stakeholders was consistent. Table 1. The results of quantitative criteria scoring by research teams and stakeholders for three orphan drugs Criteria Dimethyl fumarate Laronidase Emicizumab Research team Stakeholders Research team Stakeholders Research team Stakeholders Disease severity 4.5 4.3 5 4.6 3 4.1 Unmet need 2 3.4 4 4.3 3 3 Comparative effectiveness 0 1.4 4 3.7 4.5 2.7 Comparative safety / tolerability 2.5 1.3 -2.5 -0.5 3 2.1 Comparative patient-perceived health / patient-reported outcomes 0 1.1 4.5 3.7 4 2.5 Type of therapeutic benefit 3.5 2.3 4 3.7 3.5 3.1 Comparative cost consequences - cost of drugs 0 -0.3 -5 -2.9 -5 -1.5 Comparative cost consequences - other medical costs 0 0.8 0 0.9 0 0 Comparative cost consequences - non-medical costs 0 0.8 0 1.2 2.5 1.4 Quality of evidence 4 3.1 3.5 3.7 3 2.6 Expert consensus / clinical practice guidelines 4 2.9 5 3.7 4.5 2.9 Kendall coordination coefficient (W)* - 0.633 - 0.698 - 0.506 Progressive significance - 0 - 0 - 0 *Since the score of the research team was obtained after discussion between two members, there was no incompatibility, so there was no Kendall coordination test 3.2.2 The calculated MCDA scores Combining the empirical data scores on various drug criteria with the weight of the framework criteria, the MCDA scores of the three drugs were obtained (Table 2, Figure 2). In the score from the research team, the pharmacoempirical MCDA scores of DMF, Laronidase and Emicizumab were 0.50, 0.57 and 0.52 respectively. For stakeholder experts, the pharmacoempirical MCDA scores of them were 0.47, 0.59 and 0.51. Therefore, the value priority of the three drugs evaluated by research team and stakeholders were Laronidase, Emicizumab, and DMF. For specific drugs, in the quantitative criteria scoring of DMF, the Kendall coordination coefficient (W) of the scoring results obtained by the research team and the stakeholders was 0.948 ( P < 0.05), showing a high consistency. However, the score results obtained by the research team and the stakeholder experts differed in the criteria “Type of therapeutic benefit” and “Unmet need” (the score difference was more than 0.02 points). For Laronidase, the W value of the score results obtained by two groups was 0.994 ( P < 0.05), showing high consistency, while the scores obtained by the them differed in the criterion “Comparative cost consequences - cost of drugs” (-0.17 and -0.04). For Emicizumab, the W value of the score results was 0.982 ( P < 0.05), while there were differences in the scores for the criteria “Disease severity” (0.11 and 0.15), “Comparative effectiveness” (0.07 and 0.04), and “Comparative cost consequences - cost of drugs” (-0.02 and -0.07). In terms of the specific criteria, taking the scores from the research team as an example, for DMF, the empirical MCDA score was 0.50 points. The criterion with highest score was “Disease severity” (0.17). The disease treated by DMF is MS, which is a disease with high severity and can lead to a large loss of life years, so the high score of DMF in disease severity is consistent with the characteristics of the disease being treated. For Laronidase, the criterion with highest score was “Disease severity” (0.19), and the lowest score was the criterion “Comparative cost consequences – cost of drugs” (-0.07). The disease treated by Laronidase is MPS-Ⅰ, whose severity is also high, and the average life of patients with severe MPS-Ⅰ is less than 10 years. Since the control intervention for Laronidase was placebo, the adverse effects of itself and the drug cost must be higher, so Laronidase received negative scores on the “Comparative safety / tolerability” and “Comparative cost consequences - cost of drugs”, which is also consistent with its actual characteristics. For Emicizumab, the highest score was “Type of therapeutic benefit” (0.12), and the lowest score was the criterion “Comparative cost consequences - cost of drugs” (-0.07). The control intervention for Emicizumab was human coagulation Factor VIII, and the annual cost of Emicizumab was nearly millions more than the control intervention, so for the criterion about the cost of drugs, it received the lowest score -0.07. 3.2.3 Sensitivity analysis The sensitivity analysis was carried out on the score value of each criterion and the tornado diagrams of the three drugs were shown in Figure 3. In the sensitivity analysis of DMF, the research team's MCDA score ranged from 0.46 to 0.53 points, and stakeholder experts’ MCDA scores ranged from 0.44 to 0.51. Among different criteria, the criterion “Type of therapeutic benefit” and “Disease severity” of three drugs had a greater impact on the MCDA scores, which were related to the greater weight of the criteria. 3.3 Qualitative criteria score results The results of qualitative criteria evaluation were shown in Table 3, each qualitative criterion for drugs was scored on positive, neutral, or negative. Among them, DMF lacks the evaluation of the affordability of medical insurance fund due to the availability of data. For DMF, it received 3 positive, 3 neutral and 1 negative evaluation from research team, while 4 positive and 4 neutral evaluations from stakeholder experts. The Kendall coordination coefficient obtained by the two groups was 0.733 ( P > 0.05), which shown that there was a certain consistency between the two groups on the qualitative criteria, but the results were not statistically significant. The results were similar for the other two drugs. 4 Discussion In this study, three orphan drugs, DMF, Laronidase and Emicizumab, were selected as empirical research objects, and empirical data were collected through literature research and data calculation. After scoring by the research team and stakeholders, the model was tested empirically, and the possible problems of the framework in the two steps of empirical collection and empirical scoring were explored. In the process of collecting empirical data, we found that it was difficult to determine a scientific control intervention of drugs, which indicates that it is necessary to define a scientific and standard control intervention selection guideline for specification when the MCDA framework is applied in practice. In addition, in the collection of empirical data, due to the small number of patients with rare diseases, the empirical data such as the effectiveness is less and the quality of evidence is insufficient, so real world data (RWD) needs to be included as empirical data to improve the quality of data. Taking cost data as an example, since the control drugs in clinical trials of orphan drugs is often placebo or optimal care treatment, the very low cost of placebo results in a low score for the drug in the criterion about cost of drugs. However, in the real world, support treatments for rare diseases are varied and do not use placebos, which can make estimates different from real-world ones. It was found that there was a high degree of agreement between the scores of the research team and the stakeholders in the quantitative criteria. But stakeholder experts were less likely to give an extreme score of 5 or -5, preferring to give a "middle" score, which may be due to the supportive feedback from stakeholders who make moderate choices[17]. In terms of scoring details, for the empirical data that shows the drug is not different from the control intervention, stakeholder experts gave a score that is not "no difference" or "no data available". This may be because stakeholder experts not only acquire cognition of orphan drugs to be evaluated based on empirical data set, but also have some understanding of them in their own research, so in this situation, they will make a rating judgment based on their own cognition. Studies have shown that where there is scant and limited evidence for criteria, such as "Comparative patient-perceived health / patient-reported outcomes" and some economic outcomes, stakeholders’ scores often vary widely on these outcomes[18]. For this difference, it is necessary to conduct the corresponding sensitivity analysis for the MCDA score of the final drug regimen. Due to the differences in the personal experience and knowledge, there was a big difference in the evaluation and judgment of the qualitative criteria among different stakeholders. For example, in the criterion “Government objectives and policy priorities” of Emicizumab, 5 stakeholders believed that Emicizumab is very consistent with the policy priorities (positive), while other six stakeholders considered that it is incompatible with it (negative). This may also be related to the fact that back-to-back expert consultation letters and reviews were used in the empirical scoring process, and there was insufficient exchange of views among stakeholders. In the scoring process, some stakeholders said that it is necessary to read a large amount of empirical data to complete the scoring, which imposes a great burden on the raters' cognition and may affect the scoring effect. Since the empirical data of the research are mainly written, and it takes time to read and understand, visual presentation of empirical data may be an effective solution to this cognitive burden[19]. However, according to the results of 2022 national price negotiations for medicines in China, DMF had entered into the NRDL in the drug part during the agreement period, while Laronidase and Emicizumab are still not included. This result is somewhat different from our scoring results in this study (the value priority of the three drugs were Laronidase, Emicizumab, and DMF). Compared to DMF, for Laronidase and Emicizumab, the MCDA score of “cost of drug” were much lower, which means the drug cost of these two drugs are relatively high. In the process of national medical insurance access in China, basically the clinical treatment needs, health economic value of drugs and the sustainability of the use of medical insurance funds are taken into account in the decision-making process[20], but the economic factor is still the most important one for consideration. For the MCDA, which is a methodology for appraising alternatives on individual, often conflicting criteria, and combining them into one overall appraisal[21]. Compared to previous assessment method, it takes more relevant aspects into consideration, rather just cost and efficacy, which may also cause obstruction in the implement changes or bring some practical difficulties in the real practice. Therefore, there is still a long way to go in the application of multi-dimensional value evaluation of MCDA in reality. Nevertheless, our study had certain limitations. First, in the empirical scoring, one scoring group was two research team members, which lacked certain authority. If another stakeholder expert groups were used to score the empirical data, there would be better reliability of results. Second, in the process of empirical calculation, due to the availability of data, the calculation of drug cost and budget impact analysis assumed that patients can obtain sufficient drug doses required for treatment, but in reality, patients may struggle to afford adequate doses, resulting in the actual annual treatment cost being lower than the theoretical value. 5 Conclusion In this study, DMF, Laronidase and Emicizumab were selected for empirical study and test based on a MCDA framework for OMP value evaluation. The empirical research tests shown that the framework constructed in this study had certain operability and could be used as supplementary evidence in the real evaluation of the value for OMP. However, it is still necessary to subdivide the scoring criteria to enhance operability, while disclosing the MCDA process to enhance the transparency of decision-making. Declarations Funding: The project was financially supported by National Natural Science Foundation of China (NSFC) (Project Approval number: 72374151) Conflict of Interest: The authors declare no conflicts of interest. Availability of data and material: All data are publicly available. Ethics approval: Not applicable Consent to participate: Not applicable Consent for publication: Not applicable Code availability: Not applicable Author contributions: Concept and design: Xiang, Hu Acquisition of data: Chen, Xiang Analysis and interpretation of data: Tang, Chen, Xiang Drafting of the manuscript: Tang, Chen Critical revision of the paper for important intellectual content: Tang, Hu Supervision: Hu Tang and Chen contributed equally to this manuscript, and all authors read and approved the final version. Acknowledgement: Great gratitude is expressed to all the stakeholder experts who participated in the workshop and provided their opinions towards the development of the criteria framework and scoring process. The detailed information with regards to the experts can be found in Appendix Table 4. Support from the National Natural Science Foundation of China is gratefully acknowledged. References Friedmann C, Levy P, Hensel P, et al. Using multi-criteria decision analysis to appraise orphan drugs: a systematic review. Expert Rev Pharmacoecon Outcomes Res Apr. 2018;18(2):135–46. 10.1080/14737167.2018.1414603 . Denis A, Mergaert L, Fostier C, et al. A comparative study of European rare disease and orphan drug markets. Health Policy. 2010;97(2):173–9. 10.1016/j.healthpol.2010.05.017 . Zimmermann BM, Eichinger J, Baumgartner MR. A systematic review of moral reasons on orphan drug reimbursement. Orphanet J Rare Dis Jun. 2021;30(1):292. 10.1186/s13023-021-01925-y . 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Multiple Criteria Decision Analysis for Health Care Decision Making—An Introduction: Report 1 of the ISPOR MCDA Emerging Good Practices Task Force. Value in Health. 2016;19(1):1–13. 10.1016/j.jval.2015.12.003 . Tables Tables 2 and 3 are available in the Supplementary Files section. Supplementary Files tables2and3.docx AppendixandSupplementarymaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3724723","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345752820,"identity":"1942e772-5bd0-4e2b-99e1-7b143180683b","order_by":0,"name":"Xian Tang","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xian","middleName":"","lastName":"Tang","suffix":""},{"id":345752821,"identity":"8a73e90d-2177-4a71-83d1-7d49375dae00","order_by":1,"name":"Handong Chen","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Handong","middleName":"","lastName":"Chen","suffix":""},{"id":345752822,"identity":"c74018cf-37f1-4e25-9db8-610108406a0f","order_by":2,"name":"Yuliang Xiang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yuliang","middleName":"","lastName":"Xiang","suffix":""},{"id":345752823,"identity":"be858983-d9f3-4354-a207-56f881ad3be0","order_by":3,"name":"Ming Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACAyA+wMBgA+HxkKAljUQtQHCYBC3mEtmJhwt+nbfnn5HA+OBtG4O8OSEtljNyNxye2Xc7ccaNBGbDuW0MhjsbCDnsBlALb8/tBAOJBDZp3jaGBIMDxGk5Zw/Uwv6beC08Pw4wbgDawkycljNvgbY0JCfOOPOwWXLOOQnDDQS1HM/d/Jnnj509f3vywQ9vymzkCdoCBoxtYLIBSEgQox4E/hCrcBSMglEwCkYkAAC+I0M96VfvlwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3637-4130","institution":"Sichuan University West China School of Pharmacy","correspondingAuthor":true,"prefix":"","firstName":"Ming","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2023-12-08 09:29:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3724723/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3724723/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65347523,"identity":"f35845ed-c447-48ca-bed1-c5eb58f0a356","added_by":"auto","created_at":"2024-09-26 10:03:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29125,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ethe normalized weight of each quantitative criterion\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3724723/v1/254776eb3a3d7bb8f07e0a5a.png"},{"id":65347521,"identity":"8904c15c-8eb3-4820-890e-3f2b2a6a8a42","added_by":"auto","created_at":"2024-09-26 10:03:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87555,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ethe MCDA scores of the quantitative criteria for three orphan drugs\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3724723/v1/44e7c551d28a7f82b6176f40.png"},{"id":65347525,"identity":"680fbc6d-9215-4f44-980f-734e34b9de10","added_by":"auto","created_at":"2024-09-26 10:03:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":127228,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ethe sensitivity analysis results of the quantitative criteria for three orphan drugs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea: Dimethyl fumarate; b: Laronidase; c: Emicizumab\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3724723/v1/7cf77d3ea451f9b095f08531.png"},{"id":85941159,"identity":"395975f1-42b1-428b-98db-9dedf774949d","added_by":"auto","created_at":"2025-07-03 11:45:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1126000,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3724723/v1/33b4cfa2-1019-4917-b8ff-45d4a9bc8765.pdf"},{"id":65348292,"identity":"adfa579a-3cec-46cf-bddb-1efa237ff3e0","added_by":"auto","created_at":"2024-09-26 10:11:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19911,"visible":true,"origin":"","legend":"","description":"","filename":"tables2and3.docx","url":"https://assets-eu.researchsquare.com/files/rs-3724723/v1/077719d6199aff43af26f65e.docx"},{"id":65347526,"identity":"6e5af9c4-7154-425d-9e5e-0fdfb1fc8c99","added_by":"auto","created_at":"2024-09-26 10:03:44","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":392885,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixandSupplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-3724723/v1/e77cc907492eaabafad59a6e.docx"}],"financialInterests":"","formattedTitle":"An Empirical Study of Value Evaluation by Multi-criteria Decision Analysis for Orphan Medicinal Product","fulltext":[{"header":"Key points for decision makers","content":"\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFor rare diseases, it is difficult to use standard HTA to assess the value of orphan medicinal products due to the lack of appropriate diagnostic methods and scarcity of health professionals, as well as insufficient evidence of clinical efficacy and disease burden caused by small patient populations.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMCDA is one of the comprehensive evaluation methods of drug value, which is a systematic decision-making thinking and method that integrates decision-making concept and practice. At present, MCDA has been more and more applied to drug procurement by bidding, drug selection in essential drugs list and medical insurance drug reimbursement list, as well as the evaluation of clinical treatment methods and the selection of medical insurance programs.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThis empirical research tests shown that MCDA framework had good operability and feasibility, and it could be used as supplementary evidence in the real evaluation of the value for OMP.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"1 Introduction","content":"\u003cp\u003eEvidence-based economic evaluation is the basis of value assessment in many countries when making reimbursement decisions. However, for rare diseases, it is difficult to use standard health technology assessments (HTA) to assess the value of orphan medicinal product (OMP) due to insufficient evidence of clinical efficacy and disease burden caused by small patient populations[1-3]. Therefore, it is necessary to adopt a more comprehensive method to evaluate the value of OMP from multiple criteria. Multi-criteria decision analysis (MCDA) is one of the comprehensive evaluation methods of drug value, which is a systematic decision-making thinking and method that integrates decision-making concept and practice. At present, MCDA has been more and more applied to drug procurement by bidding, drug selection in essential drugs list and medical insurance drug reimbursement list, and other related fields. Internationally, MCDA is increasingly advocated as a method to evaluate the value of OMP[4-6]. Iskrov G et al. (2014)[7]\u0026nbsp;proposed the use of MCDA in the field of OMP, and found that MCDA could improve the fairness and strictness of orphan drug reimbursement decisions. Many studies have also found that MCDA can overcome the above defects in the current HTA for OMP, and can be used by health insurance decision-makers[8-10]. Gilabert-Perramon et al. (2017)[11]\u0026nbsp;constructed a Catalan value evaluation framework for OMP based on the EVIDEM framework in MCDA, and the results showed that MCDA (EVIDEM framework) was helpful and could improve the understanding and transparency for decision-making. Vandewalle B et al. (2021)[12]\u0026nbsp;evaluated the value of Burosumab in the treatment of X-linked hypophosphataemia based on MCDA, and found that MCDA could consider the drug value criteria that not included in the traditional economic evaluation, and the expert consistency was high.\u003c/p\u003e\n\u003cp\u003eIn the actual implementation of national medical insurance access evaluation in China, although priority is given to OMPs, the access procedures and evaluation methods for OMP are consistent with those for conventional drugs. That is, the cost-effectiveness analysis results in economic evaluation are compared with the threshold determined based on GDP, and the budget impact analysis results are also considered. Combined with the current status of medical insurance affordability, drugs suitable for inclusion in medical insurance reimbursement are finally determined through negotiation of price reduction[13]. For innovative orphan drugs, how to comprehensively balance the value, accessibility, affordability, and build a multi-criteria value evaluation framework suitable for medical insurance access is a hot topic in recent years. Yuan et al. (2021) constructed the evaluation index system of medical insurance access for OMP in China through literature research and expert consultation[13]. The study found that the medical insurance access evaluation index of OMP should be multi-criteria and focused on the treatment demand, actual treatment effect, social impact and equity on the basis of considering the cost-effectiveness. Based on the EVIDEM framework in MCDA, Zhang et al. (2022) organized experts in related fields to formulate the Expert Consensus on the Application of Multi-criteria Decision Analysis to the Clinical Comprehensive Evaluation of orphan medical product (2022), which provided normative methodological guidance for the clinical comprehensive evaluation of OMP[14]\u0026nbsp;. However, there is no empirical study and framework test on the constructed framework in Chinese studies, lacking of application examples. In addition, most of the current empirical studies in China and abroad are comparative studies of different drugs treating the same rare disease, and there are few studies on how to compare the value of innovative drugs for treating different rare diseases.\u003c/p\u003e\n\u003cp\u003eThis study intends to conduct an empirical study on the value evaluation of three specific orphan drugs that have been marketed and indicated in China, by using the constructed MCDA based valuation framework for OMP. The selected drugs are Dimethyl fumarate for multiple sclerosis, Laronidase for mucopanosaccharidosis, and Emicizumab for hemophilia. Through the empirical data collection and scoring based on the value evaluation framework for OMP, the feasibility of the constructed MCDA criterion framework was tested, and the scoring results of the three drugs were compared.\u003c/p\u003e"},{"header":"2 Method ","content":"\u003cp\u003e\u003cstrong\u003e2.1 Construct and assign the weight to the MCDA criterion framework for OMP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the criteria framework of drug value evaluation for OMP that suitable for China based on MCDA was first constructed. We obtained the initial version of the framework based on the criteria of EVIDEM (V.10) framework, by doing some localized translation, and also taking the current Chinese medical insurance policy, characteristics of OMP, and ethical of each criterion into consideration. Then, 15 experts in related fields were invited as members of the stakeholder expert panel to discuss and give advice to the preliminary framework through brainstorming method. After revision, the final framework of OMP value evaluation criteria based on the opinions of stakeholders and operational conditions was formed (Appendix Table 1 in Supplemental Materials).\u003c/p\u003e\n\u003cp\u003eThen, stakeholder experts assigned the weight to each quantitative criterion in the constructed criteria framework by the two-step percentile distribution method. The weighting results were shown in Appendix Figure 1 in Supplemental Materials, and more process and method details were shown in ours another analysis article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Empirical study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1 Empirical drug selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultiple sclerosis (MS) is an incurable, chronically progressive autoimmune disease that occurs in the central nervous system and was included in the first Rare Diseases Catalogue (2018) in China. At present, several drugs for MS have been marketed in China, such as interferon beta-1a/1b, Fingolimod, Teriflunomide, Dimethyl fumarate, Fampridine, Siponimod, etc. Among them, Dimethyl fumarate (DMF) and Fingolimod, among the oral small molecule drugs, are widely used in clinical application[15], and Fingolimod was approved for marketing in China in 2020 for the treatment of relapse multiple sclerosis (RMS) patients aged 10 years or older, and was included in National Reimbursement Drug List (NRDL) in 2020. DMF was approved for market in China in April 2021, and also can be used clinically for the treatment of adult patients with RMS, and has not been included in the NRDL in China by 2022.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMucopolysaccharidosis (MPS) was divided into 11 subtypes and was included in the Catalogue in China. The main treatment methods for MPS-Ⅰ are hematopoietic stem cell transplantation and enzyme replacement therapy (ERT)[16]. Laronidase is the world\u0026apos;s first and only approved ERT for MPS-I, which was approved and marketed in China in June 2020, and it is the only specific therapeutic drug for the treatment of MPS-I in China. It can be used for MPS-I patients of all ages and different disease severity, filling the gap of specific therapeutic drugs and the unmet clinical demand, and it has not been included in the NRDL in China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHaemophilia is an inherited hemorrhagic disease characterized by disorder in the production of coagulation factor Ⅷ (FⅧ), in which haemophilia type A accounts for 80%~85% of all hemophilia. At present, the listed hemophilia drugs in China include Emicizumab, Recombinant Human Coagulation Factor Ⅷ/Ⅶ for Injection, Human Coagulation Factor Ⅷ, and Human Prothrombin Complex. Only Emicizumab is the monoclonal drug, which was approved in November 2018 in China. Its efficacy is better than that of other drugs, but it has not been included in the NRDL in China.\u003c/p\u003e\n\u003cp\u003eTherefore, DMF, Laronidase and Emicizumab was chosen to do the empirical study, as they all treat rare diseases, have certain advantages in the treatment of corresponding diseases, and are expected to be included in NRDL in China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2 Empirical data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEmpirical data included information about the treated diseases (disease severity and unmet need), clinical effects and economic outcomes of the drugs, quality of evidence, clinical guidelines, government objectives and policy priorities, and affordability of medical insurance funds. Data sources and search terms were shown in Appendix Table 2 in Supplemental Materials, and specific collection details are shown below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisease information:\u003c/strong\u003e The indications of rare diseases of the corresponding drugs were used as search terms, and relevant clinical research and disease review literature was collected. Also some disease data collected from the sample hospital (West China Hospital).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical effects of the drug:\u0026nbsp;\u003c/strong\u003eThe name of the empirical drugs and their control drugs were used as search terms. Relevant clinical studies and systematic reviews were collected, combined with clinical effect data provided by drug manufacturers and sample hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEconomic results:\u0026nbsp;\u003c/strong\u003eThe name of the empirical drugs and their control drugs, and pharmacoeconomics or market research were used as search terms, to collect drug related cost data in China market. If there is no relevant study, we will do the treatment cost estimate. The drug price is from the drug bidding database of yaozhi.com, and the median price of drugs winning the bid in 2021 was selected, combined with the treatment course in the drug instructions, and the treatment cost data from sample hospital, then calculated the treatment cost.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality of evidence:\u003c/strong\u003e The research team evaluated the evidence quality of the collected clinical and economic outcomes and summarized the quality, reliability and uncertainty of the available evidence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Guidelines and Expert Consensus:\u003c/strong\u003e Using the corresponding indications of rare diseases and clinical guidelines or expert consensus as the keywords, collected the recommendations of the evaluated drugs in the treatment guidelines and expert consensus.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCommon goals and specific interests:\u003c/strong\u003e The names of the empirical drugs, their control drugs and the disease were used as keywords, news reports of interest groups related to the rare diseases were collected and summarized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGovernment objectives and policy priorities:\u003c/strong\u003e By searching Bailu Thinktank and taking the names of empirical drugs, their control drugs and disease as keywords, the relevant national policies of OMP were collected and summarized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAffordability of medical insurance\u0026nbsp;fund:\u003c/strong\u003e Since there are no Chinese budget impact analysis (BIA) studies related to the three drugs, this study conducted BIA for them. Drug market data are provided from enterprises, and drug prices are obtained from the database of yaozhi.com and sample hospital. The number of patients with diseases treated by drugs was retrieved from Pubmed, Web of Science, and Chinese Journal Database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOther criteria data:\u003c/strong\u003e The name of the empirical drugs, their control drugs, disease and other related search terms were used to search.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter collecting all the data, the data were summarized into a preliminary empirical data set according to the criteria entry. Then the stakeholder experts discussed the problems existing and the evidence to be supplemented, and put forward opinions and suggestions. After being supplemented and refined based on expert opinions, formed the final empirical data set (Appendix Table 3 in Supplemental Materials).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.3 Empirical performance scoring\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe empirical data of the three drugs were scored by the research team and the stakeholder experts group respectively, which can help explore the possible problems encountered in the empirical scoring step of the framework, and also compare whether the two teams scored consistently. Then the weight obtained from the stakeholder perspective were used to calculate the final weighted score of the drugs.\u003c/p\u003e\n\u003cp\u003eFor the scoring of research team, two members independently scored the performance of each drug on each criterion based on empirical data results. After that, the third member summarized the scoring results and organized discussion, and finally obtained the empirical scores of each criterion for the three drugs. Stakeholder evaluation adopted letter evaluation questionnaire, and invited stakeholder experts to score the performance of the drugs in each criterion based on the empirical data set and their own judgment. In quantitative criteria, scoring the drug\u0026apos;s performance in each criterion on a scale of 0 to 5 (non-comparative criteria) or -5 to 5 (comparative criteria). In the qualitative criteria, choosing the most appropriate qualitative evaluation options according to the actual situation of the drug.\u003c/p\u003e\n\u003cp\u003eMicrosoft Excel 2019 and SPSS17.0 were used for statistical analysis and calculate the MCDA scores of each drug. The value contribution (Vx) of each quantitative criterion was then calculated as the product of its normalised scoring (Wx, \u0026sum;Wx = 1) and standardised score (Sx = score/5). The overall MCDA value estimate (V) of each orphan drugs was calculated based on a linear additive model as a sum the value contributions (Vx) of all (n) criteria of the quantitative criteria.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"200\" height=\"36\"\u003e\u003c/p\u003e\n\u003cp\u003eThe consistency of the MCDA scores calculated by the research team and the stakeholder expert scoring was compared. The degree of coordination of criterion scores was evaluated by Kendall\u0026apos;s coordination coefficient (W).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Sensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sensitivity analysis was carried out on the quantitative criteria of MCDA framework. The score of the research team and stakeholders in each criterion was added and subdivided by 1 point as the upper and lower limits (the highest limit was 5 points, and the lowest limit was 0 or -5 points), and the sensitivity analysis was carried out on the score value of each criterion.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 The constructed value evaluation framework for OMP based on MCDA and the weighting results for the quantitative criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the stakeholder experts\u0026rsquo; opinion, the final OMP value evaluation criteria framework consists 11 quantitative criteria and 8 qualitative criteria, see in the previous article, and Appendix Table 1 in Supplemental Materials. Based on two-step percentile distribution method, the results of the weight of each quantitative criterion were shown in Appendix Figure 1 in Supplemental Materials. The total weight of all criteria was set to 1 and the individual criterion weights were normalized, shown in Figure 1. For this part, more detailed process and analysis were shown in another article of our research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 MCDA score for quantitative criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1 Scoring results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe empirical data scoring results of DMF, Laronidase and Emicizumab by the research team and stakeholders were shown in Table 1. There are some differences between the scoring results from stakeholder and research team, for example, the research team scores have many higher compared with the scores from stakeholders, also with some extreme values, like 5 and -5. For the stakeholder experts, the scoring Kendall\u0026apos;s coordination coefficient (W) of DMF, Laronidase and Emicizumab by stakeholders were 0.633, 0.698 and 0.506, respectively, and their progressive significance was 0.00, less than 0.05, indicating that the scoring of these three orphan drugs by stakeholders was consistent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. The results of quantitative criteria scoring by research teams and stakeholders for three orphan drugs\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"616\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 157px;\"\u003e\n \u003cp\u003eCriteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 157px;\"\u003e\n \u003cp\u003eDimethyl fumarate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003eLaronidase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003eEmicizumab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eResearch team\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eStakeholders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eResearch team\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eStakeholders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eResearch team\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eStakeholders\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eDisease severity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eUnmet need\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eComparative effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eComparative safety / tolerability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eComparative patient-perceived health / patient-reported outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eType of therapeutic benefit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eComparative cost consequences - cost of drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eComparative cost consequences - other medical costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eComparative cost consequences\u0026nbsp;- non-medical costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eQuality of evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eExpert consensus / clinical practice guidelines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eKendall coordination coefficient (W)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eProgressive significance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Since the score of the research team was obtained after discussion between two members, there was no incompatibility, so there was no Kendall coordination test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2 The calculated MCDA scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCombining the empirical data scores on various drug criteria with the weight of the framework criteria, the MCDA scores of the three drugs were obtained (Table 2, Figure 2). In the score from the research team, the pharmacoempirical MCDA scores of DMF, Laronidase and Emicizumab\u0026nbsp;were 0.50, 0.57 and 0.52 respectively. For stakeholder experts, the pharmacoempirical MCDA scores of them were 0.47, 0.59 and 0.51. Therefore, the value priority of the three drugs evaluated by research team and stakeholders were Laronidase, Emicizumab, and DMF.\u003c/p\u003e\n\u003cp\u003eFor specific drugs, in the quantitative criteria scoring of DMF, the Kendall coordination coefficient (W) of the scoring results obtained by the research team and the stakeholders was 0.948 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), showing a high consistency. However, the score results obtained by the research team and the stakeholder experts differed in the criteria \u0026ldquo;Type of therapeutic benefit\u0026rdquo; and \u0026ldquo;Unmet need\u0026rdquo; (the score difference was more than 0.02 points). For Laronidase, the W value of the score results obtained by two groups was 0.994 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), showing high consistency, while the scores obtained by the them differed in the criterion \u0026ldquo;Comparative cost consequences - cost of drugs\u0026rdquo; (-0.17 and -0.04). For Emicizumab, the W value of the score results was 0.982 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), while there were differences in the scores for the criteria \u0026ldquo;Disease severity\u0026rdquo; (0.11 and 0.15), \u0026ldquo;Comparative effectiveness\u0026rdquo; (0.07 and 0.04), and \u0026ldquo;Comparative cost consequences - cost of drugs\u0026rdquo; (-0.02 and -0.07).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn terms of the specific criteria, taking the scores from the research team as an example, for DMF, the empirical MCDA score was 0.50 points. The criterion with highest score was \u0026ldquo;Disease severity\u0026rdquo; (0.17). The disease treated by DMF is MS, which is a disease with high severity and can lead to a large loss of life years, so the high score of DMF in disease severity is consistent with the characteristics of the disease being treated. For Laronidase, the criterion with highest score was \u0026ldquo;Disease severity\u0026rdquo; (0.19), and the lowest score was the criterion \u0026ldquo;Comparative cost consequences \u0026ndash; cost of drugs\u0026rdquo; (-0.07). The disease treated by Laronidase is MPS-Ⅰ, whose severity is also high, and the average life of patients with severe MPS-Ⅰ is less than 10 years. Since the control intervention for Laronidase was placebo, the adverse effects of itself and the drug cost must be higher, so Laronidase received negative scores on the \u0026ldquo;Comparative safety / tolerability\u0026rdquo; and \u0026ldquo;Comparative cost consequences - cost of drugs\u0026rdquo;, which is also consistent with its actual characteristics. For Emicizumab, the highest score was \u0026ldquo;Type of therapeutic benefit\u0026rdquo; (0.12), and the lowest score was the criterion \u0026ldquo;Comparative cost consequences - cost of drugs\u0026rdquo; (-0.07). The control intervention for Emicizumab was human coagulation Factor VIII, and the annual cost of Emicizumab was nearly millions more than the control intervention, so for the criterion about the cost of drugs, it received the lowest score -0.07.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.3 Sensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sensitivity analysis was carried out on the score value of each criterion and the tornado diagrams of the three drugs were shown in Figure 3. In the sensitivity analysis of DMF, the research team\u0026apos;s MCDA score ranged from 0.46 to 0.53 points, and stakeholder experts\u0026rsquo; MCDA scores ranged from 0.44 to 0.51. Among different criteria, the criterion \u0026ldquo;Type of therapeutic benefit\u0026rdquo; and \u0026ldquo;Disease severity\u0026rdquo; of three drugs had a greater impact on the MCDA scores, which were related to the greater weight of the criteria.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Qualitative criteria score results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of qualitative criteria evaluation were shown in Table 3, each qualitative criterion for drugs was scored on positive, neutral, or negative. Among them, DMF lacks the evaluation of the affordability of medical insurance fund due to the availability of data. For DMF, it received 3 positive, 3 neutral and 1 negative evaluation from research team, while 4 positive and 4 neutral evaluations from stakeholder experts. The Kendall coordination coefficient obtained by the two groups was 0.733 (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05), which shown that there was a certain consistency between the two groups on the qualitative criteria, but the results were not statistically significant. The results were similar for the other two drugs.\u003c/p\u003e"},{"header":"4 Discussion ","content":"\u003cp\u003eIn this study, three orphan drugs, DMF, Laronidase and Emicizumab, were selected as empirical research objects, and empirical data were collected through literature research and data calculation. After scoring by the research team and stakeholders, the model was tested empirically, and the possible problems of the framework in the two steps of empirical collection and empirical scoring were explored.\u003c/p\u003e\n\u003cp\u003eIn the process of collecting empirical data, we found that it was difficult to determine a scientific control intervention of drugs, which indicates that it is necessary to define a scientific and standard control intervention selection guideline for specification when the MCDA framework is applied in practice. In addition, in the collection of empirical data, due to the small number of patients with rare diseases, the empirical data such as the effectiveness is less and the quality of evidence is insufficient, so real world data (RWD) needs to be included as empirical data to improve the quality of data. Taking cost data as an example, since the control drugs in clinical trials of orphan drugs is often placebo or optimal care treatment, the very low cost of placebo results in a low score for the drug in the criterion about cost of drugs. However, in the real world, support treatments for rare diseases are varied and do not use placebos, which can make estimates different from real-world ones.\u003c/p\u003e\n\u003cp\u003eIt was found that there was a high degree of agreement between the scores of the research team and the stakeholders in the quantitative criteria. But stakeholder experts were less likely to give an extreme score of 5 or -5, preferring to give a \u0026quot;middle\u0026quot; score, which may be due to the supportive feedback from stakeholders who make moderate choices[17]. In terms of scoring details, for the empirical data that shows the drug is not different from the control intervention, stakeholder experts gave a score that is not \u0026quot;no difference\u0026quot; or \u0026quot;no data available\u0026quot;. This may be because stakeholder experts not only acquire cognition of orphan drugs to be evaluated based on empirical data set, but also have some understanding of them in their own research, so in this situation, they will make a rating judgment based on their own cognition. Studies have shown that where there is scant and limited evidence for criteria, such as \u0026quot;Comparative patient-perceived health / patient-reported outcomes\u0026quot; and some economic outcomes, stakeholders\u0026rsquo; scores often vary widely on these outcomes[18]. For this difference, it is necessary to conduct the corresponding sensitivity analysis for the MCDA score of the final drug regimen.\u003c/p\u003e\n\u003cp\u003eDue to the differences in the personal experience and knowledge, there was a big difference in the evaluation and judgment of the qualitative criteria among different stakeholders. For example, in the criterion \u0026ldquo;Government objectives and policy priorities\u0026rdquo; of Emicizumab, 5 stakeholders believed that Emicizumab is very consistent with the policy priorities (positive), while other six stakeholders considered that it is incompatible with it (negative). This may also be related to the fact that back-to-back expert consultation letters and reviews were used in the empirical scoring process, and there was insufficient exchange of views among stakeholders.\u003c/p\u003e\n\u003cp\u003eIn the scoring process, some stakeholders said that it is necessary to read a large amount of empirical data to complete the scoring, which imposes a great burden on the raters\u0026apos; cognition and may affect the scoring effect. Since the empirical data of the research are mainly written, and it takes time to read and understand, visual presentation of empirical data may be an effective solution to this cognitive burden[19].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, according to the results of 2022 national price negotiations for medicines in China, DMF had entered into the NRDL in the drug part during the agreement period, while Laronidase and Emicizumab are still not included. This result is somewhat different from our scoring results in this study (the value priority of the three drugs were Laronidase, Emicizumab, and DMF). Compared to DMF, for Laronidase and Emicizumab, the MCDA score of \u0026ldquo;cost of drug\u0026rdquo; were much lower, which means the drug cost of these two drugs are relatively high. In the process of national medical insurance access in China, basically the clinical treatment needs, health economic value of drugs and the sustainability of the use of medical insurance funds are taken into account in the decision-making process[20], but the economic factor is still the most important one for consideration. For the MCDA, which is a methodology for appraising alternatives on individual, often conflicting criteria, and combining them into one overall appraisal[21]. Compared to previous assessment method, it takes more relevant aspects into consideration, rather just cost and efficacy, which may also cause obstruction in the implement changes or bring some practical difficulties in the real practice. Therefore, there is still a long way to go in the application of multi-dimensional value evaluation of MCDA in reality.\u003c/p\u003e\n\u003cp\u003eNevertheless, our study had certain limitations. First, in the empirical scoring, one scoring group was two research team members, which lacked certain authority. If another stakeholder expert groups were used to score the empirical data, there would be better reliability of results. Second, in the process of empirical calculation, due to the availability of data, the calculation of drug cost and budget impact analysis assumed that patients can obtain sufficient drug doses required for treatment, but in reality, patients may struggle to afford adequate doses, resulting in the actual annual treatment cost being lower than the theoretical value.\u003c/p\u003e"},{"header":"5 Conclusion ","content":"In this study, DMF, Laronidase and Emicizumab were selected for empirical study and test based on a MCDA framework for OMP value evaluation. The empirical research tests shown that the framework constructed in this study had certain operability and could be used as supplementary evidence in the real evaluation of the value for OMP. However, it is still necessary to subdivide the scoring criteria to enhance operability, while disclosing the MCDA process to enhance the transparency of decision-making."},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe project was financially supported by National Natural Science Foundation of China (NSFC) (Project Approval number: 72374151)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003eAll data are publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConcept and design:\u003c/em\u003e Xiang, Hu\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcquisition of data:\u003c/em\u003e Chen, Xiang\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnalysis and interpretation of data:\u003c/em\u003e Tang, Chen, Xiang\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDrafting of the manuscript:\u003c/em\u003e Tang, Chen\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCritical revision of the paper for important intellectual content:\u0026nbsp;\u003c/em\u003eTang, Hu\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSupervision:\u003c/em\u003e Hu\u003c/p\u003e\n\u003cp\u003eTang and Chen contributed equally to this manuscript, and all authors read and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u0026nbsp;\u003c/strong\u003eGreat gratitude is expressed to all the stakeholder experts who participated in the workshop and provided their opinions towards the development of the criteria framework and scoring process. The detailed information with regards to the experts can be found in Appendix Table 4. Support from the National Natural Science Foundation of China is gratefully acknowledged.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFriedmann C, Levy P, Hensel P, et al. Using multi-criteria decision analysis to appraise orphan drugs: a systematic review. Expert Rev Pharmacoecon Outcomes Res Apr. 2018;18(2):135\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/14737167.2018.1414603\u003c/span\u003e\u003cspan address=\"10.1080/14737167.2018.1414603\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDenis A, Mergaert L, Fostier C, et al. A comparative study of European rare disease and orphan drug markets. 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Value in Health. 2016;19(1):1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jval.2015.12.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jval.2015.12.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 2 and 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3724723/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3724723/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo conduct an empirical study on the comprehensive value of specific orphan drugs based on the constructed Multi-criteria decision analysis (MCDA) framework, evaluate the feasibility of applying MCDA to evaluate value of orphan drugs, and provide reference for expanding the decision-making ideas and evidence of medical insurance access for orphan drugs.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eDimethyl fumarate, Laronidase, and Emicizumab were selected as empirical drugs, and collected their empirical data by literature retrieval, third-party data extraction and enterprise consultation. The empirical drugs were scored independently by the research team and stakeholder group based on the constructed MCDA framework and weight, also combined with qualitative evaluation and finally analyzed the results of the MCDA empirical evaluation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the quantitative criteria evaluation, the calculated MCDA scores of the three drugs were 0.50, 0.57 and 0.52 respectively by the research team, and 0.47, 0.59 and 0.51 by the stakeholder group, which shows the high scoring consistency of two groups, and the obtained value priority of the quantitative criteria of the three drugs from high to low is Laronidase, Emicizumab, and Dimethyl fumarate. In the qualitative criteria evaluation, the evaluation results of the two groups did not achieve statistically significant consistency, but for the criteria \u0026ldquo;Mandate and scope of healthcare system\u0026rdquo; and \u0026ldquo;Affordability of medical insurance fund\u0026rdquo;, they scored the same.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe empirical study proves that MCDA is feasible in the evaluation of orphan drug value in China, and can be used as a supplementary tool for the access decision of medical insurance drugs.\u003c/p\u003e","manuscriptTitle":"An Empirical Study of Value Evaluation by Multi-criteria Decision Analysis for Orphan Medicinal Product","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-26 10:03:39","doi":"10.21203/rs.3.rs-3724723/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"28569fd4-31bd-4906-9563-e2d3b99902bc","owner":[],"postedDate":"September 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-03T11:37:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-26 10:03:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3724723","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3724723","identity":"rs-3724723","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}