•  Because decision conferencing can generate both the outcome and a description of the process that leads to the outcome, it can improve stakeholders’ and the public’s understanding of a regulatory decision.


MCDA is a set of methods designed to bring together evaluations of options on different criteria into one overall decision (CHMP, 2008). There are many variants of MCDA, some of which may be adapted to consider the uncertainty of the decision-maker (Linkov and Seager, 2011), and MCDA can be used to provide either a qualitative or a quantitative assessment. The basic methods of MCDA are scoring and weighting (CHMP, 2008). Scoring involves the process of assigning numerical values to options according to particular criteria. Weighting ensures the comparability of the numerical values assigned to all criteria, which allows comparison of different health states with a single metric (Linkov and Seager, 2011). The weights assigned to scores reflect the relative importance of the underlying criteria for the benefit–risk assessment outcome (Walker et al., 2005).

MCDA uses four steps (Linkov and Seager, 2011):

1.  Defining the problem and the decision context.

2.  Identifying stakeholders, decision-makers, assessment criteria (for example, health outcomes of interest), and the relative importance of different health outcomes.

3.  Defining and assessing management alternatives whereby the effects of different regulatory decisions on each criterion, or health outcome, are assessed.

4.  Allowing for variability in weighting of different criteria and accounting for the stochastic nature of data through the use of probabilistic sensitivity analysis to provide a rank order of different alternatives for distinct stakeholder groups.

Those four steps help to ensure that all participants understand and are in agreement about the need for a regulatory decision, the criteria by which benefit–risk balance is judged, the evidence and its uncertainties, the values and preferences of different stakeholders, and the consequences of different regulatory decisions. The outputs or information synthesis of the benefit–risk assessment stage of the framework should include model inputs and model outputs (Linkov and Seager, 2011). The model inputs would include estimates of benefits, estimates of risks, the degree of uncertainty, and preferences for health outcomes based on ethical values. The model outputs may be characterized quantitatively, for example, the benefits of a drug outweigh the risks 85 percent of the time; or qualitatively, for example, there is clear and convincing evidence that the benefits of a drug outweigh its risks. The synthesis should also discuss any uncertainty

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