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UID:1519@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20170116T140000
DTEND;TZID=Europe/Paris:20170116T150000
DTSTAMP:20170101T130000Z
URL:https://www.i2m.univ-amu.fr/evenements/handling-uncertainty-around-the
 -incremental-cost-utility-ratio-accounting-for-mapping-problem/
SUMMARY: (...): Handling uncertainty around the Incremental Cost Utility Ra
 tio accounting for Mapping problem
DESCRIPTION:: Objectives: Firstly\, we recall how to handle uncertainty in 
 medico-economic evaluations in general\, in the aim of providing a reliabl
 e decision-making in terms of allocating resources. In particular\, the me
 thod we developed to build confidence regions around the Incremental Cost-
 Effectiveness Ratio\, solving three problems simultaneously is presented: 
 1) the mathematical problem of instabilities of some methods when the deno
 minator of the ratio approaches zero statistically\, 2) the “mirror deci
 sion-making” problem where two opposite ratios provide the same decision
 \, and finally 3) Interpretation in terms of decision-making of confidence
  regions having non standard form with Fieller’s method.Actually\, the c
 ost-utility analyses (CUA) are internationally recommended by the National
  Institute for Health and Care Excellence. Utility measure accounts for pa
 tient preferences and their quality of life by measuring Quality Adjusted 
 Life Years\, which are gained years multiplying by utility or preference s
 cores. This makes more complex the handling of uncertainty.In addition\, i
 n CUA\, utility values are rarely available and they are generally predict
 ed using a “mapping” interpolation from a functional status questionna
 ire. This mapping method is not accounted for in pharmaceutical industry a
 nd in literature studies\, when building confidence regions around the inc
 remental cost-utility ratio\, leading to a wrong confidence region and con
 sequently\, to a wrong decision-making. Thus\, the purpose of this researc
 h is to build a confidence region around the Incremental Cost-Utility Rati
 o\, accounting for the uncertainty coming from the “mapping” interpola
 tion.Methods: Analytical\, parametric and nonparametric Bootstrap methods 
 are developed to handle the fact that utility values are interpolated. Lin
 ear\, multilinear\, and nonlinear mapping are considered and compared to a
  “naïve” method\, used in practice\, not accounting for mapping. Mont
 e Carlo experiments are carried out to compare the performance of these va
 rious methods\, which are then applied on data issued from a clinical tria
 l about hepatitis C treatment\, measuring the impact of therapeutic educat
 ion. Utility values are assessed from a SF-12 questionnaire and some of th
 ese values are interpolated from the Nottingham Health Profile functional 
 status questionnaire.Results: Monte Carlo experiments show that the analyt
 ic and bootstrap 95% CI display coverage between 94% and 96% for various s
 ample sizes. If mapping is not accounted for (“naive method”)\, the co
 verage is between 61% and 95%. The cross validation shows similar results.
  Conclusion: In CUA\, decision-making based on utility values interpolated
  from mapping is not reliable and the uncertainty due to mapping has to be
  accounted for. Our analytic and bootstrap procedures\, integrating the ma
 pping\, provide very accurate results.http://ispb.univ-lyon1.fr->http://is
 pb.univ-lyon1.fr/recherche/enseignants-chercheurs/recherche-cv-siani-82093
 3.kjsp?RH=1321948729222#.WHjaN7F7QUE
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