MULTI-CRITERIA DECISION ANALYSIS FROM THE COST-EFFECTIVENESS PERSPECTIVE- IS MCDA A COHERENT WAY TO ALLOCATE HEALTHCARE RESOURCES?
Author(s)
O'Mahony JF
Trinity College Dublin, Dublin, Ireland
BACKGROUND: Multi-criteria decision analysis (MCDA) is attracting growing interest as a potential alternative to conventional cost-effectiveness analysis (CEA). It can be used to assess the broader range of attributes that decision makers typically consider alongside costs and health effects. However, the application MCDA presents profound practical questions that demand careful consideration. OBJECTIVE: To describe four key challenges to MCDA as a coherent guide to healthcare resource allocation. ANALYSIS: Firstly, several attributes proposed for inclusion within MCDA can be disputed as spurious. For example, it is questionable whether innovation, disease rarity and budget impact should be accepted as valid attributes. Secondly, the inclusion of attributes in a weighted sum MCDA approach requires quantification of the attributes' level. How such attributes should be quantified is unclear in many cases. Thirdly, there are good reasons to suggest that cost-effectiveness should be represented within MCDA using a net benefit measure, rather than cost-effectiveness ratios. Net benefit is an absolute metric that varies with the size of the patient population. Consequently it is unclear how an absolute measure should be weighed against more abstract concepts such as disease severity or unmet need. Finally, to achieve a coherent resource allocation framework it is essential that any decision rule employing MCDA account for the opportunity cost of health effects and other benefits foregone in the interventions not funded. Accounting for a broader scope of benefits in the opportunity cost requires a reduction in the cost-effectiveness threshold. CONCLUSIONS: There are strong arguments in favour of expanding the scope of benefits included in CEA beyond health effects. Nevertheless, this work shows that there are a number of profound questions for the application of MCDA in healthcare resource allocations that must be resolved before it can be considered a suitable replacement for current CEA methods.
Conference/Value in Health Info
2016-10, ISPOR Europe 2016, Vienna, Austria
Value in Health, Vol. 19, No. 7 (November 2016)
Code
PRM222
Topic
Methodological & Statistical Research
Topic Subcategory
Confounding, Selection Bias Correction, Causal Inference
Disease
Multiple Diseases