The role of economic evaluation in allocation of resources is presented. With the use of Bayesian decision analytical modeling it is possible to determine whether the introduction of the studied intervention into clinical practice is warranted and whether more resources should be allocated to further research in this area. The methodology is applied to the costeffectiveness assessment of breast cancer screening. A Markov model was build for 36 different screening policies and for no screening policy. Uncertainty of the model inputs was transferred to the uncertainty of the model results with probabilistic sensitivity analysis. With the presented analysis, it was shown that a 1-year screening interval in population breast cancer screening would produce less benefits at higher costs than less intensive screening and that a 2-year interval would be cost-effective only at high values of society’s willingness to pay per quality adjusted life-year (QALY). Therefore, based on commonly quoted thresholds of society’s willingness to pay per QALY, the optimal approach in Slovenian population would be screening women aged from 40 to 80 years every 3 years. The incremental cost-effectiveness ratio of this policy is around €13.000/QALY. But the probabilistic analysis revealed relatively high uncertainty of the expected values for costs and effects of different policies. Therefore, the value of additional information was deemed reasonably high, indicating that further research would be warranted. The partial value of information for the groups of parameters indicated that future research would be most valuable if directed toward obtaining more precise estimates of the cancer sojourn times. Further analysis of opportunity loss indicated that the benefits of additional research would be smaller than opportunity loss due to the delay of the decision. Therefore, the best option would be to implement the most cost-effective policy given the existing information (screening women aged 40–80 years, at 3-year intervals) and simultaneously conduct observational studies alongside the implemented policy. The decision analytic model could be in this manner periodically updated with additional information as it became available and the most cost-effective policy chosen iteratively. Theoretically appropriate methodology with many advantages was used on a real world example. But this approach has also some limitations, with computational demand being one of the biggest. This difficulty was successfully resolved with the use of Gaussian process
metamodels, which were deemed the most appropriate. With their usage, the time of the analysis was shortened by more than 99%, that is from projected 44 years to 47 days. By doing so, the practical value of the presented methodology for resource allocation in health care was shown.
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