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Yu L, Yang H, Lu L, Fang Y, Zhang X, Li S, Li C. Developing mapping algorithms to predict EQ-5D health utility values from Bath Ankylosing Spondylitis Disease Activity Index and Bath Ankylosing Spondylitis Functional Index among patients with Ankylosing Spondylitis. Health Qual Life Outcomes 2024; 22:61. [PMID: 39113080 PMCID: PMC11304938 DOI: 10.1186/s12955-024-02276-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 07/19/2024] [Indexed: 08/11/2024] Open
Abstract
BACKGROUND Preference-based measures of health-related quality of life (HRQoL), such as the EQ-5D or the SF-6D, are essential for health economic evaluation. However, they are rarely included in clinical trials of ankylosing spondylitis (AS). This study aims to develop mapping algorithms to predict EQ-5D-3L and EQ-5D-5L health utility scores from the Bath Ankylosing Disease Activity Index (BASDAI) and the Bath Ankylosing Spondylitis Functional Index (BASFI). METHODS Patients with AS were recruited from the largest tertiary hospital in Shandong province, China, between December 2019 and October 2020. Patients were selected by convenience sampling method according to the following criteria: (1) diagnosed with AS according to the New York criteria; (2) aged 18 years and above; and (3) without mental disorders; (4) able to understand the questionnaires; (5) without serious complications. There were 243 patients who completed the face-to-face questionnaire survey, and 5 cases with missing values in key variables were excluded. Ordinary least squares, censored least absolute deviations, Tobit, adjusted limited dependent variable mixture model and beta-mixture model (BM) in the direct approach and ordered logit and multinomial logit (Mlogit) model in the response approach were used to develop mapping algorithms. Mean absolute error, root mean square error, Spearman's correlation coefficient and concordance correlation coefficient were used to access predictive performance. RESULTS The 238 patients with AS had a mean age of 35.19 (SD = 9.59) years, and the majority (74.47%) were male. The observed EQ-5D-3L and EQ-5D-5L health utility values were 0.88 (SD = 0.12) and 0.74 (SD = 0.27), respectively. The EQ-5D-5L had higher conceptual overlap with the BASDAI and BASFI than the EQ-5D-3L did. The Mlogit was the best-performing model for the EQ-5D-3L, and the BM showed better performance in predicting EQ-5D-5L than other direct and indirect mapping models did. CONCLUSION This study demonstrates that the EQ-5D-5L, rather than EQ-5D-3L, should be selected as the target outcome measure of HRQoL in patients with AS in China, and the BM mapping algorithm could be used to predict EQ-5D-5L values from BASDAI and BASFI for health economic evaluation.
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Affiliation(s)
- Lingjia Yu
- Nursing Department, Rheumatology department, Qilu hospital of Shandong University, Jinan, 250012, China
| | - Huizhi Yang
- Shunyi District Center for Disease Control and Prevention, Beijing, 101300, China
| | - Liyong Lu
- Centre for Health Management and Policy Research, School of Public Health, Cheeloo College of Medicine, Shandong University, Wenhua Xi Road 44, Jinan, 250012, China
- NHC Key Lab of Health Economics and Policy Research, Shandong University, Wenhua Xi Road 44, Jinan, 250012, China
- Center for Health Preference Research, Shandong University, Wenhua Xi Road 44, Jinan, 250012, China
| | - Yingying Fang
- Nursing Department, Rheumatology department, Qilu hospital of Shandong University, Jinan, 250012, China
| | - Xianyu Zhang
- Nursing Department, Rheumatology department, Qilu hospital of Shandong University, Jinan, 250012, China
| | - Shunping Li
- Centre for Health Management and Policy Research, School of Public Health, Cheeloo College of Medicine, Shandong University, Wenhua Xi Road 44, Jinan, 250012, China
- NHC Key Lab of Health Economics and Policy Research, Shandong University, Wenhua Xi Road 44, Jinan, 250012, China
- Center for Health Preference Research, Shandong University, Wenhua Xi Road 44, Jinan, 250012, China
| | - Chaofan Li
- Centre for Health Management and Policy Research, School of Public Health, Cheeloo College of Medicine, Shandong University, Wenhua Xi Road 44, Jinan, 250012, China.
- NHC Key Lab of Health Economics and Policy Research, Shandong University, Wenhua Xi Road 44, Jinan, 250012, China.
- Center for Health Preference Research, Shandong University, Wenhua Xi Road 44, Jinan, 250012, China.
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Su P, Zhi K, Xu H, Xiao J, Liu J, Wang Z, Liu Q, Yu Y, Dang H. The application of multi-criteria decision analysis in evaluating the value of drug-oriented intervention: a literature review. Front Pharmacol 2024; 15:1245825. [PMID: 38720775 PMCID: PMC11076741 DOI: 10.3389/fphar.2024.1245825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 04/10/2024] [Indexed: 05/12/2024] Open
Abstract
Objectives: Multi-Criteria Decision Analysis (MCDA) has gained increasing attention in supporting drug risk-benefit assessment, pricing and reimbursement, as well as optimization of clinical interventions. The objective of this study was to systematically collect and categorize evaluation criteria and techniques of weighting and scoring of MCDA for drug value assessment. Methods: A systematic review of the literature was conducted across seven databases to identify articles utilizing the MCDA frameworks for the evaluation of drug value. Evaluation criteria mentioned in the included studies were extracted and assigned to 5 dimensions including clinical, economic, innovative, societal and humanistic value. A descriptive statistical analysis was performed on the identified drug value evaluation criteria, as well as the weighting and scoring techniques employed. The more a criterion or technique were mentioned in articles, the more important we consider it. Results: Out of the 82 articles included, 111 unique criteria were identified to evaluate the value of drug. Among the 56 unique criteria (448 times) used to measure clinical value, the most frequently mentioned were "comparative safety/tolerability" (58 times), "comparative effectiveness/efficacy" (56 times), "comparative patient-perceived health/patient reported outcomes" (37 times), "disease severity" (34 times), and "unmet needs" (25 times). Regarding economic value measurement, out of the 20 unique criteria (124 times), the most frequently utilized criteria were "cost of intervention" (17 times), "comparative other medical costs" (16 times), and "comparative non-medical costs" (18 times). Out of the 10 criteria (18 times) for assessing innovative value, "a novel pharmacological mechanism" was the most frequently mentioned criterion (5 times). Among the 22 criteria (73 times) used to measure societal value, "system capacity and appropriate use of intervention" was the most frequently cited criterion (14 times). Out of the 3 criteria (15 times) utilized to measure humanistic value, "political/historical/cultural context" was the most frequently mentioned criterion (9 times). Furthermore, 11 scoring and 11 weighting techniques were found from various MCDA frameworks. "Swing weighting" and "a direct rating scale" were the most frequently used techniques in included articles. Conclusion: This study comprehensively presented the current evaluation dimensions, criteria, and techniques for scoring and weighting in drug-oriented MCDA articles. By highlighting the frequently cited evaluation criteria and techniques for scoring and weighting, this analysis will provide a foundation to reasonably select appropriate evaluation criteria and technique in constructing the MCDA framework that aligns with research objectives.
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Affiliation(s)
- Pengli Su
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Kai Zhi
- China Academy of Chinese Medical Sciences, Beijing, China
| | - Huanhuan Xu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Jing Xiao
- School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Jun Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zhong Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Qiong Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yanan Yu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Haixia Dang
- China Academy of Chinese Medical Sciences, Beijing, China
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Liu S, Xia Y, Yang Y, Ming J, Sun H, Wei Y, Chen Y. Mapping of health technology assessment in China: a comparative study between 2016 and 2021. Glob Health Res Policy 2024; 9:4. [PMID: 38229176 PMCID: PMC10790493 DOI: 10.1186/s41256-023-00339-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 12/09/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND Health Technology Assessment (HTA) in China has recently expanded from purely academic research to include policy or decision-oriented practice, especially after HTA evidence was used to update the National Reimbursement Drug List for the first time in 2017. This study aims to identify the progress and challenges of HTA development from 2016 to 2021 and inform policies and decisions to promote further HTA development in China. METHODS We conducted a cross-sectional web-based survey with policy makers, researchers and industry-providers in China in 2016 and 2021 respectively. The 'Mapping of HTA Instrument', was utilized to assess the HTA development across eight domains: Institutionalization, Identification, Priority setting, Assessment, Appraisal, Reporting, Dissemination of findings and conclusions, and Implementation in policy and practice. To reduce the influence of confounders and compare the mapping outcomes between the 2016 and 2021 groups, we conducted 1:1 Propensity Score Matching (PSM). Univariate analysis was conducted to compare the differences between the two groups. The overall results were further compared with those of a mapping study that included ten countries. RESULTS In total, 212 and 255 respondents completed the survey in 2016 and 2021, respectively. The total score of the HTA development level in China in 2021 was higher than that in 2016 before PSM (89.38 versus 83.96). Following PSM, 183 respondents from the 2016 and 2021 groups were matched. Overall, the mean scores for most indicators in the Institutionalization domain and Dissemination domain in 2021 were higher than those in 2016 (P < 0.05). The Appraisal domain in 2021 was more explicit, transparent and replicable than that in 2016 (t = -3.279, P < 0.05). However, the mean scores of most indicators in the Assessment domain were higher in 2016 than those in 2021 (P < 0.05). CONCLUSIONS Our study suggest that the level of HTA development in China progressed significantly from 2016 to 2021. However, before engaging in HTA activities, further efforts are required to enhance the assessment process. For instance, it is important to establish a clear goal and scope for HTA; adapt standardized methodologies for evaluating the performance of systematic reviews or meta-analyses; and provide comprehensive descriptions of the safety, clinical effectiveness, cost, and cost-effectiveness of the assessed technologies, thus improving the development of HTA in China.
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Affiliation(s)
- Shimeng Liu
- School of Public Health, Fudan University, Shanghai, 200032, China
- National Health Commission Key Laboratory of Health Technology Assessment (Fudan University), Shanghai, 200032, China
| | - Yu Xia
- School of Public Health, Fudan University, Shanghai, 200032, China
- National Health Commission Key Laboratory of Health Technology Assessment (Fudan University), Shanghai, 200032, China
| | - Yi Yang
- School of Public Health, Fudan University, Shanghai, 200032, China
- National Health Commission Key Laboratory of Health Technology Assessment (Fudan University), Shanghai, 200032, China
| | - Jian Ming
- School of Public Health, Fudan University, Shanghai, 200032, China
- National Health Commission Key Laboratory of Health Technology Assessment (Fudan University), Shanghai, 200032, China
| | - Hui Sun
- School of Public Health, Fudan University, Shanghai, 200032, China
- National Health Commission Key Laboratory of Health Technology Assessment (Fudan University), Shanghai, 200032, China
- Shanghai Health Development Research Center, Shanghai, 201199, China
| | - Yan Wei
- School of Public Health, Fudan University, Shanghai, 200032, China.
- National Health Commission Key Laboratory of Health Technology Assessment (Fudan University), Shanghai, 200032, China.
| | - Yingyao Chen
- School of Public Health, Fudan University, Shanghai, 200032, China.
- National Health Commission Key Laboratory of Health Technology Assessment (Fudan University), Shanghai, 200032, China.
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