Multi-criteria methods have been extensively used to support individual and group decisions in a wide variety of areas, including medical decision making. The approach allows decision makers to make transparent judgments based on numerical scores. Measures of consistency are also available. These are combined into a numerical score using a weighting process that accounts for direct and indirect comparisons.
#Benefit from using expert choice series#
After structuring the decision model, a series of pairwise comparisons is used to determine the relative importance of the criteria relative to the decision goal, and the importance of treatment options relative to the criteria. It incorporates quantitative and qualitative criteria into the decision process. The AHP, pioneered by Saaty, is one such approach that is potentially useful in group decision making. MCDA techniques can be used to structure complex decisions and improve the transparency of the decision making process. This method results in a transparent decision making process so that groups or individuals using this method can understand and demonstrate the underpinnings of their decisions, in contrast to standard decision making processes in which the importance of the various components of the decision is not explicit. The Analytic Hierarchy Process (AHP) is a commonly-used MCDA method which incorporates benefits and risks explicitly by combining the importance of differences in probabilities of outcomes related to (treatment) alternatives and the weighting of the importance of those outcomes. Multicriteria decision analysis (MCDA) refers to a class of model-based methods particularly useful for benefit-risk analysis in the face of multiple objectives (e.g., treatment-related outcomes). Given the complexity of medication decision-making in type 2 diabetes, a quantitative method which integrates evidence on treatment-related benefits and harms with preferences regarding trade-offs between benefits and risks should be useful to support decisions. Consistent with this shift in thinking in diabetes management, the FDA issued draft guidance in 2013 on the structured assessment of benefit and risk with the goal of facilitating drug regulatory decisions which are explicit and transparent. The recent combined statement from the American Diabetes Association and the European Association for the Study of Diabetes on patient-centered approaches to hyperglycemia in patients with type 2 diabetes highlights the importance of shared decision-making that considers all aspects of treatment options, including preferences for benefits and harms.
Since views on the importance of treatment-related outcomes vary across patients, providers, regulatory decision-makers, and other stakeholders, both the probabilities of these heterogeneous outcomes and their importance to stakeholders must be considered when making decisions about diabetes medications. Beyond this multitude of choices, each medication class, and even a medication within a class, has different benefits and harms these treatment-related benefits and harms are often unknown at the time of initial Food and Drug Administration (FDA) approval, and they occur at different time points in the course of therapy. While metformin is the clear first-line medication for the pharmacologic treatment of type 2 diabetes, the choice of add-on medications is vast with 11 additional classes available. Relative differences between treatment alternatives for minimizing risk of bladder cancer. Relative differences between alternatives for minimizing risk of acute pancreatitis. Relative differences between alternatives for minimizing risk of CHF. Relative differences between alternatives for minimizing risk of severe hypoglycemia.
Relative differences between alternatives for minimizing GI symptoms. Relative differences between alternatives for minimizing weight gain. Relative differences between alternatives for minimizing risk of fracture. Relative differences between alternatives for maximizing reduction of HbA1c. Relative differences between objectives at third level of hierarchy*.
Data on objectives for treatment alternatives.
0 kommentar(er)
