Prioritizing Healthcare Interventions: A Comparison of Multicriteria Decision Analysis and Cost-Effectiveness Analysis
| Autor: | Yana Pryymachenko, Anupa Pathak, Jason Chua, J. Haxby Abbott, Ross Wilson, Saurab Sharma |
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| Rok vydání: | 2021 |
| Předmět: |
Multicriteria decision
Adult Computer science Cost-Benefit Analysis Population Decision Making Psychological intervention Spearman's rank correlation coefficient Decision Support Techniques Risk Factors Surveys and Questionnaires Health care Humans education Aged Aged 80 and over education.field_of_study Actuarial science business.industry Health Policy Public Health Environmental and Occupational Health Cost-effectiveness analysis Middle Aged Models Theoretical Osteoarthritis Knee Multiple-criteria decision analysis Treatment Outcome Ranking Quality-Adjusted Life Years business Delivery of Health Care New Zealand |
| Zdroj: | Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. 25(2) |
| ISSN: | 1524-4733 |
| Popis: | Objectives To investigate the extent to which stated preferences for treatment criteria elicited using multicriteria decision analysis (MCDA) methods are consistent with the trade-offs (implicitly) applied in cost-effectiveness analysis (CEA), and the impact of any differences on the prioritization of treatments. Methods We used existing MCDA and CEA models developed to evaluate interventions for knee osteoarthritis in the New Zealand population. We established equivalent input parameters for each model, for the criteria “treatment effectiveness,” “cost,” “risk of serious harms,” and “risk of mild-to-moderate harms” across a comprehensive range of (hypothetical) interventions to produce a complete ranking of interventions from each model. We evaluated the consistency of these rankings between the 2 models and investigated any systematic differences between the (implied) weight placed on each criterion in determining rankings. Results There was an overall moderate-to-strong correlation in intervention rankings between the MCDA and CEA models (Spearman correlation coefficient = 0.51). Nevertheless, there were systematic differences in the evaluation of trade-offs between intervention attributes and the resulting weights placed on each criterion. The CEA model placed lower weights on risks of harm and much greater weight on cost (at all accepted levels of willingness-to-pay per quality-adjusted life-year than did respondents to the MCDA survey. Conclusions MCDA and CEA approaches to inform intervention prioritization may give systematically different results, even when considering the same criteria and input data. These differences should be considered when designing and interpreting such studies to inform treatment prioritization decisions. |
| Databáze: | OpenAIRE |
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