Abstract
When conducting an experimental study in healthcare systems, two problems are faced, those of uncertainty and complexity. Uncertainty is related to identifying variables for data collection (particularly if there are time and cost constraints on the modelling exercise). Complexity is related to the existence of many interacting variables (including treatment paths for patients, patient illnesses, side effects of treatments, etc.), each of a stochastic nature. This paper reports the usefulness of discrete event simulation modelling in exploring these issues. It focuses on the use of this form of simulation in supporting decision making in a randomised clinical trial (RCT). The objective of using simulation modelling is to help health economists identify the key factors active in the RCT through the development of a model of the healthcare related processes being studied by the RCT. This approach provides an opportunity to allow users to understand the role of these factors in the RCT. This research is carried out in the context of the Adjuvant Breast Cancer RCT.