OBJECTIVES: The main objective of this study was to assess when adverse events (AEs) are important to implement or not in health-economic (HE) models. METHODS: The approach described in this work investigated in HE models the impact on the incremental cost-effectiveness ratio (ICER) of including or not AEs. The simulations have been realized on 29 existing models covering a wide range of therapeutic area: oncology (solid tumor (12) and hematology (5)), urology (1), neurology (10) and dermatology (1). Models were both deterministic and probabilistic. Simulations were done by applying a null-value to costs and disutilities to all AEs at once. RESULTS: In 21 out of the 29 simulations, the ICERs did not vary by more than 10%. In this group of low impact, the mean percentage of variation was 1.7%. In the other group of results (8), half were very close to 10% and half greater than 25%. Regarding the link with the therapeutic area, the top 4 percentages of variations came from the simulations in urology and neurology. In oncology, only 2 situations led to a percentage of variation of around 10%. Results were also calculated for costs and QALY (Quality-Adjusted-Life-Year) separately. CONCLUSIONS: When analyzing the 4 situations in which modeling AEs or not had the biggest impact, it was due to a combination of factors such as: high frequency of AEs and their severity relative to the modelled disease and with respect to the comparators. In oncology, the cost and severity of AEs could be considered negligible compared to the costs of treatment and severity of the disease. The detailed analysis of these 29 situations can provide insight of when it is meaningful or not to include AEs. Sensitivity analysis should be conducted in very early stages of model development to guide resource allocations in developing HE studies.