ArticlePharmacoEconomics2017
Weighing Clinical Evidence Using Patient Preferences: An Application of Probabilistic Multi-Criteria Decision Analysis.
Article in PharmacoEconomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Multimethod quantitative benefit-risk assessment of treatments for moderate-to-severe osteoarthritis.British journal of clinical pharmacology · 2022Pooled it
- Multi-criteria decision analysis for health technology assessment: addressing methodological challenges to improve the state of the art.The European journal of health economics : HEPAC : health economics in prevention and care · 2019Pooled it
- A multi‑criteria decision analysis comparing robot‑assisted, laparoscopic, and open radical cystectomy using large-scale Japanese nationwide databases.Journal of robotic surgery · 2026Article
- Personalization of Medical Treatment Decisions: Simplifying Complex Models while Maintaining Patient Health Outcomes.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Article
- A comparison of various aggregation functions in multi-criteria decision analysis for drug benefit-risk assessment.Statistical methods in medical research · 2022Article
- Partial Personalization of Medical Treatment Decisions: Adverse Effects and Possible Solutions.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Article
- Personalizing Medical Treatment Decisions: Integrating Meta-analytic Treatment Comparisons with Patient-Specific Risks and Preferences.Medical decision making : an international journal of the Society for Medical Decision Making · 2019Article
- A novel measure of drug benefit-risk assessment based on Scale Loss Score.Statistical methods in medical research · 2019Article
- Patient-Focused Drug Development Methods for Benefit-Risk Assessments: A Case Study Using a Discrete Choice Experiment for Antiepileptic Drugs.Clinical pharmacology and therapeutics · 2019Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The need for patient engagement has been recognized by regulatory agencies, but there is no consensus about how to operationalize this. One approach is the formal elicitation and use of patient preferences for weighing clinical outcomes. The aim of this study was to demonstrate how patient preferences can be used to weigh clinical outcomes when both preferences and clinical outcomes are uncertain by applying a probabilistic value-based multi-criteria decision analysis (MCDA) method. Probability distributions were used to model random variation and parameter uncertainty in preferences, and parameter uncertainty in clinical outcomes. The posterior value distributions and rank probabilities for each treatment were obtained using Monte-Carlo simulations. The probability of achieving the first rank is the probability that a treatment represents the highest value to patients. We illustrated our methodology for a simplified case on six HIV treatments. Preferences were modeled with normal distributions and clinical outcomes were modeled with beta distributions. The treatment value distributions showed the rank order of treatments according to patients and illustrate the remaining decision uncertainty. This study demonstrated how patient preference data can be used to weigh clinical evidence using MCDA. The model takes into account uncertainty in preferences and clinical outcomes. The model can support decision makers during the aggregation step of the MCDA process and provides a first step toward preference-based personalized medicine, yet requires further testing regarding its appropriate use in real-world settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.