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PP072 Applying Sensitivity Analysis For Robust Choice Of Health Technologies
Published online by Cambridge University Press: 12 January 2018
Abstract
The aim of this work is to evaluate the stability and robustness of the solution obtained at the end of the Health Technology Assessment (HTA) process by conducting a sensitivity analysis. Sensitivity analysis allows identification of the elements representing the source of uncertainty and to determine the impact of this variability on the stability of the assessment results, in order to provide more adequate and objective support to decision-making process.
A new method for health technologies evaluation, Decision-oriented HTA (1), which integrates the Analytic Hierarchy Process (AHP) (2) in the model Core Model® of the European Network for HTA (EUnetHTA) was taken into account. In this context, a mathematical model was implemented to conduct a sensitivity analysis on weights and on performance values of the technology alternatives evaluated. The objective is to evaluate the effects on AHP results induced by a change on initial values of each criterion of the decision-making model. Sensitivity analysis was carried out by calculating the minimum changes of the weights and performances needed to reverse the current ranking of alternatives technologies (3).
This approach was applied to some technology assessment studies such as video-laparoscopy, femtosecond laser, da Vinci robot, to test their efficacy and reliability. It is very important to perform a sensitivity analysis and assure the stability of the solution when the performance values associated to the technology alternatives are close because, in this case, a small change of performance values reversed the ranking of alternatives technologies.
Applying sensitivity analysis to such decision-making processes is essential to ensure the consistency of final decisions. This evidence has shown that this method allows for a more rapid interpretation of results, thus facilitating the choice of decision-makers about the decision to invest or not in new technology.
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