Multi-criteria decision analysis (MCDA)

Multi-criteria decision analysis (MCDA) is being proposed to be part of health technology assessment (HTA) because it offers an opportunity to consider a comprehensive set of benefits compared with conventional HTA methods such as cost-effectiveness (CEA), which summarizes benefits in a single number. MCDA has been piloted by various HTA agencies, including G-BA, NICE, HITAP, etc.1?CEA involves a trade-off between interventions based on cost and benefits, but from the perspective of a healthcare decision maker, it is equally important to consider goals other than QALY maximization and efficiency. These goals can be reducing inequalities or the need for new interventions for severe diseases than mild severity. Also, societal perspective and outcomes can play an important role in cases such as clinicians preferring better outcomes in the pediatric population than older patients. MCDA method is efficient in systematically assigning a numerical value to these outcomes and quantifying the overall benefit of an intervention with several desired outcomes from different perspectives.2,3?Hence it can be used to include factors that cannot be incorporated into the cost-effectiveness framework. MCDA also has a few challenges, for example, it contains too many pairwise comparisons, which may have challenges due to alternatives' interdependence, causing inconsistencies between criteria and classification. Also, MCDA with decision rules may involve more deliberation around the remaining criteria that are not included in decision rules, leading to less consistent and transparent recommendations if not well documented.2?Additionally, MCDA does not provide a clear method by which to assign weights, meanwhile CEA has a well-defined method and value-assigning framework. On the other hand, for rare diseases, the effects beyond the direct treatment effect and indirect costs can be of relatively high importance, and this can lead to an unfair view of the actual cost-effectiveness of an orphan drug.

The main advantages of MCDA are:3,4

• The MCDA process is transparent. decisions at all stages for weighting and sensitivity analysis are quantitative. Through sensitivity analyses, the degree to which a disagreement affects final result can be understood, which enables the group to agree about the output while preserving individual differences in the inputs.
• The MCDA process is auditable and updatable. Also, any trade-offs can be analyzed at any point in time. It is also easier to systematically incorporate new data into the model.
• The MCDA process can include several clinical data, patient experience, clinitian perspective, and uncertainty. Also useful in certain situations to force explicit judgments rather than implicit ones so that points of agreement and differences in opinion can be easily seen and discussed.

MCDA can be the best way to decide on new health technology by identifying a preferred option and ranking or distinguishing an acceptable from an unacceptable option.?It can be efficiently used to provide an order to various options, from most preferred to least preferred, which can guide HTA agencies in the allocation of resources.

For successfully utilizing MCDA, Inappropriate procedures for weighting must be addressed. Also, there is a need for the development of methodological and practical guidelines for creating good-quality MCDA models. Ultimately, it is a good approach compared to the traditional method because it allows for the inclusion of the views of diverse stakeholder groups, such as patient groups, for making a systematic decision for healthcare agencies.

?References:

1. Marsh, K.D., Sculpher, M., Caro, J.J. and Tervonen, T., 2018. The use of MCDA in HTA: great potential, but more effort needed.?Value in Health,?21(4), pp.394-397.
2. Baran-Kooiker, A., Czech, M. and Kooiker, C., 2018. Multi-criteria decision analysis (MCDA) models in health technology assessment of orphan drugs—a systematic literature review. Next steps in methodology development?.?Frontiers in Public Health,?6, p.287.
3. Oliveira, M.D., Mataloto, I. and Kanavos, P., 2019. Multi-criteria decision analysis for health technology assessment: addressing methodological challenges to improve the state of the art.?The European Journal of Health Economics,?20, pp.891-918.
4. Thokala, P. and Duenas, A., 2012. Multiple criteria decision analysis for health technology assessment.?Value in health,?15(8), pp.1172-1181.