OBJECTIVES Prior to the actual implementation, trial simulations are often performed to optimize registration study design and hence to maximize the probability of marketing approval. Almost exclusively, trials focus on clinical outcomes however reimbursement submissions require health economic evidence, in particular, information on patients’ quality of life (QoL) and estimates of quality-adjusted life years (QALYs). Thus, considerations on QoL outcomes in the clinical trial design phase may lead to better optimized reimbursement submissions. The objective of this study was to develop a trial simulation model that is capable of addressing complex research questions, provides flexibility to test various assumptions, and predicts expected QALY outcomes. METHODS A patient-level simulation model was developed using hypothetical data in oncology. The model considered two treatments reflecting the common design of a pivotal trial. Individual survival times and time to progression data were simulated. Hazard ratios were used to include treatment effects. Using the simulated individual level data, a multistate life table model was constructed with three health states: pre-progression (with and without adverse events), post-progression, and dead. Utility and disutility values derived from literature were attached to the number of patients in each health state at a given point in time. Differences between the treatment arms were derived in terms of survival, QALYs, and the uncertainty around those (e.g. probability distribution, P-value). RESULTS The trial simulation model assessed various patient number scenarios to obtain the smallest sample size that provided a statistically significant minimum clinically meaningful QALY difference between the treatments. Simulations were performed (e.g. testing the effect of different survival profile scenarios, utility values) to assess the robustness of the results. CONCLUSIONS The presented trial simulation model provided a flexible tool to inform clinical trial design considering QoL outcomes. The model can be also useful for manufacturers for pricing or investment decisions.