Vakaramoko Diaby1, Vassiki Sanogo2, Kouame Richard Moussa3. 1. a 1 Florida A&M University-College of Pharmacy and Pharmaceutical Sciences, Tallahassee, Florida, USA. 2. b 2 Florida State University, 3200 Commonwealth Blvd. Suite, 153 Tallahasee, Tallahasee, Florida 32303, USA. 3. c 3 Université Cergy-Pontoise-Théorie Économique, Modélisation et Applications 33, boulevard du Port, Cergy-Pontoise Cedex, 95011, France.
Abstract
OBJECTIVE: In this paper, the readers are introduced to ELICIT, an imprecise weight elicitation technique for multicriteria decision analysis for healthcare. METHODS: The application of ELICIT consists of two steps: the rank ordering of evaluation criteria based on decision-makers' (DMs) preferences using the principal component analysis; and the estimation of criteria weights and their descriptive statistics using the variable interdependent analysis and the Monte Carlo method. The application of ELICIT is illustrated with a hypothetical case study involving the elicitation of weights for five criteria used to select the best device for eye surgery. RESULTS: The criteria were ranked from 1-5, based on a strict preference relationship established by the DMs. For each criterion, the deterministic weight was estimated as well as the standard deviation and 95% credibility interval. CONCLUSIONS: ELICIT is appropriate in situations where only ordinal DMs' preferences are available to elicit decision criteria weights.
OBJECTIVE: In this paper, the readers are introduced to ELICIT, an imprecise weight elicitation technique for multicriteria decision analysis for healthcare. METHODS: The application of ELICIT consists of two steps: the rank ordering of evaluation criteria based on decision-makers' (DMs) preferences using the principal component analysis; and the estimation of criteria weights and their descriptive statistics using the variable interdependent analysis and the Monte Carlo method. The application of ELICIT is illustrated with a hypothetical case study involving the elicitation of weights for five criteria used to select the best device for eye surgery. RESULTS: The criteria were ranked from 1-5, based on a strict preference relationship established by the DMs. For each criterion, the deterministic weight was estimated as well as the standard deviation and 95% credibility interval. CONCLUSIONS: ELICIT is appropriate in situations where only ordinal DMs' preferences are available to elicit decision criteria weights.
Entities:
Keywords:
Monte Carlo method; healthcare; imprecise eight elicitation; multi-criteria decision analysis; principal component analysis; variable interdependent analysis
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