Probabilistic sensitivity analysis for evaluating cost-utility of entacapone for Parkinson’s disease

Abstract

The objective was to assess uncertainty in a cost-utility analysis of adjunct entacapone treatment with levodopa among Parkinson’s disease patients by probabilistic sensitivity analysis using second-order simulation methods. The cost-effectiveness of two treatment alternatives of Parkinson’s disease – levodopa with or without entacapone – was compared in a cost-utility analysis employing a Markov model. Monte Carlo simulation was used to quantify the uncertainty due to sampling variation. The results strengthened confidence in the conclusions that entacapone as an adjunctive treatment to levodopa is both cost-saving and increases the quality of life of Parkinson’s disease patients.

• cost-utility analysis
• entacapone
• Markov model
• Parkinson’s disease
• simulation