OBJECTIVES: In cost-utility analyses (CUA), utility values are rarely available for the entire patients sample and they are generally predicted using a “mapping” interpolation from a functional status questionnaire, known for the entire sample. This mapping method is not accounted for in pharmaceutical industry and in literature studies, when building confidence regions around the utility and the incremental cost-utility ratio, leading to a wrong confidence region and consequently, to a wrong decision-making. The purpose of this paper is to build a confidence interval (CI) around the mean utility, accounting for the uncertainty coming from the “mapping” interpolation. METHODS:  Analytical and Bootstrap methods are developed to handle the fact that values are interpolated. Linear, multilinear, and nonlinear mapping are considered. Monte Carlo experiments are carried out to compare the performance of these methods. These methodologies are applied on data issued from an observational study dealing with prostate cancer treatment. Utility is assessed with Standard Gamble method and some of these values are interpolated from the questionnaires: EORTC QLQC-30; IPSS and IIEF-5; SF-36 and Visual Analogic Scales. RESULTS:  Monte Carlo experiments show that the analytic and bootstrap 95% CI display coverage between 94% and 96% for various sample sizes. If mapping is not accounted for (“naive method”), the coverage is between 20% and 40%. The cross validation shows similar results. From prostatectomy data, the utility is explained by SF-36, Role functioning, Diarrhoea and age.  For instance, mean utility equals 0.94. The analytic and bootstrap CIs equal [0.59,1.51] and [0.51,1.63] respectively. The naive interval equals [0.95,1.15].  CONCLUSIONS: In CUA, decision-making based on utility values interpolated from mapping is not reliable: a naive interval would lead to a serious mistake. The uncertainty due to mapping has to be accounted for. Our analytic and bootstrap procedures, integrating the mapping, provide very accurate results.