PATHWAYS OF IMPLEMENTATION OF MULTI-CRITERIA DECISION ANALYSIS INTO ORPHAN DRUG APPROVAL PROCEDURE FOR DRUG SUPPLY PROGRAMS IN RUSSIAN FEDERATION
Author(s)
Serpik VG1, Yagudina RI2
1NATIONAL RESEARCH INSTITUTE OF PUBLIC HEALTH, Moscow, Russia, 2First Moscow State Medical University named after I. M. Sechenov, Moscow, Russia
Background: While the orphan drug supply program is in progress, development of decision-making rules for approving orphan drug for supply program of Russian Federation becomes very actual. Real world data provides evidence, that routine approaches for approving such kind of drugs, e.i. pharmacoeconomic conclusions, are not applicable. Than the need in more appropriate approaches is existed. Multi-criteria decision analysis is one such approaches (MCDA). Objective: To evaluate prospective of implementation of MCDA in healthcare system of Russian Federation and to develop road map of MCDA in Russia. Methods: Literature review, cluster analysis, interviewing experts. Results: The first step (qualitive) to implement MCDA is to test various MCDA methods to find out optimal one for Russian Federation: it is expected to select the most relevant criteria from the wide range of them. First of all, MCDA is considered to be the instrument to improve the quality of discussion and its transparency, to underline different point of view and unmet needs. On the second stage it may be possible to use quantity MCDA assessment as a rule to approve orphan drugs for drug supply programs. Local recommendations for MCDA in Russian Federation has been published. Conclusion: Implementation of MCDA as assisting instrument for orphan drug approving for drug supply programs is likely to be a valuable approach, that may improve the quality, transparency of decision-making process and to provide social equity for accepting decisions.
Conference/Value in Health Info
2014-11, ISPOR Europe 2014, Amsterdam, The Netherlands
Value in Health, Vol. 17, No. 7 (November 2014)
Code
PRM250
Topic
Methodological & Statistical Research
Topic Subcategory
Confounding, Selection Bias Correction, Causal Inference
Disease
Rare and Orphan Diseases