ArticleHealth economics review2016
Measuring patients' priorities using the Analytic Hierarchy Process in comparison with Best-Worst-Scaling and rating cards: methodological aspects and ranking tasks.
Article in Health economics review, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Comparing patient and healthcare professional preferences for patient-centered integrated care in China: a best-worst scaling study.BMC health services research · 2026Article
- Prioritizing delirium risk factors in nursing: a cross-sectional study using the analytic hierarchy process.BMC nursing · 2025Article
- Treatment Preference for Alzheimer's Disease: A Multicriteria Decision Analysis with Caregivers, Neurologists, and Payors.Neurology and therapy · 2023Article
- Goal-oriented care for patients with chronic conditions or multimorbidity in primary care: A scoping review and concept analysis.PloS one · 2022Article
- Integrating patient perspectives in medical decision-making: a qualitative interview study examining potentials within the rare disease information exchange process in practice.BMC medical informatics and decision making · 2019Article
- Assessing the Importance of Treatment Goals in Patients with Psoriasis: Analytic Hierarchy Process vs. Likert Scales.The patient · 2018Article
- Shaping an Effective Health Information Website on Rare Diseases Using a Group Decision-Making Tool: Inclusion of the Perspectives of Patients, Their Family Members, and Physicians.Interactive journal of medical research · 2017Article
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5 authors.
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Abstract
backgroundIdentifying patient priorities and preference measurements have gained importance as patients claim a more active role in health care decision making. Due to the variety of existing methods, it is challenging to define an appropriate method for each decision problem. This study demonstrates the impact of the non-standardized Analytic Hierarchy Process (AHP) method on priorities, and compares it with Best-Worst-Scaling (BWS) and ranking card methods.
methodsWe investigated AHP results for different Consistency Ratio (CR) thresholds, aggregation methods, and sensitivity analyses. We also compared criteria rankings of AHP with BWS and ranking cards results by Kendall's tau b.
resultsThe sample for our decision analysis consisted of 39 patients with rare diseases and mean age of 53.82 years. The mean weights of the two groups of CR ≤ 0.1 and CR ≤ 0.2 did not differ significantly. For the aggregation by individual priority (AIP) method, the CR was higher than for aggregation by individual judgment (AIJ). In contrast, the weights of AIJ were similar compared to AIP, but some criteria's rankings differed. Weights aggregated by geometric mean, median, and mean showed deviating results and rank reversals. Sensitivity analyses showed instable rankings. Moderate to high correlations between the rankings resulting from AHP and BWS. LIMITATIONS: Limitations were the small sample size and the heterogeneity of the patients with different rare diseases.
conclusionIn the AHP method, the number of included patients is associated with the threshold of the CR and choice of the aggregation method, whereas both directions of influence could be demonstrated. Therefore, it is important to implement standards for the AHP method. The choice of method should depend on the trade-off between the burden for participants and possibilities for analyses.
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