The level of uncertainty with regards to the outcomes of a pharmacoeconomic study cannot be covered completely by the statistical methods routinely employed to handle uncertainty in clinical research. Sensitivity analysis is the most common methodology to deal with the extra uncertainty associated with pharmacoeconomics, which has also been incorporated in recent guidelines on health care evaluation. However the execution of a sensitivity analysis and the interpretation of the results is not yet standardised , which may lead to subjectivity and consequently weakens the value of economic evaluations, The concept of point-sensitivity was used to develop a method, which allows a more objective judgement of the robustness of a model by avoiding the subjective in most current economic studies. However, a limitation of this method is that the sensitivity measurement is based on a uniform distribution of the variables, which may actually have different distributions. A second limitation is that the overall sensitivity measure is based on a subjectively chosen range, which excludes the impact of values outside the range on the overall sensitivity. The present study presents a refinement of the method by the incorporation of probability distributions, which allow a more accurate assessment of the level of uncertainty in the model. In addition, a bootstrap method is used to create probability distribution for fixed input variables based on a limited number of data points.