OBJECTIVES: We developed an individual-based micro-simulation model for radiotherapy treatment in non small-cell lung cancer (NSCLC). The aim was to explore the suitability of multi-state statistical modelling in heath economics, as a tool to parameterize a simulation model that tracks clinical events over time, taking patient and tumour features into account. METHODS: The model contains the four clinical states ‘A: alive without local recurrence (LR) or metastasis (M)’, ‘LR’, ‘M’, and ‘Death’. Transition rates were estimated using multi-state statistical modelling, a technique that allows the simultaneous estimation of hazards for multiple transitions, taking covariates as well as the occurrence and timing of previous events into account. Each of the hazards from A to either LR, M and Death were adjusted for the presence of the other competing risks. Individual patients were simulated by repeatedly sampling a patient profile, consisting of patient and tumour characteristics. Subsequently, for each patient a pathway through the model was simulated. The internal validity of the model was verified by comparing intermediate simulation outcomes and overall survival under two different radiotherapy strategies to the original data used for estimation. Finally, the model was externally validated by comparing model outcomes to Dutch cancer registry data. RESULTS: Model simulations for the two radiotherapy strategies demonstrated internal validity, with predicted probabilities for the occurrence of LRs, Ms, deaths, and the occurrence of toxicities within 3 years that fell within the 95% confidence intervals of the data. The same was observed for the prediction of overall survival. Comparison of the model predictions to the Dutch cancer registry data showed a moderate fit. CONCLUSIONS: Multi-state statistical modelling is a useful technique for obtaining the transition rates that are required for the quantification of a micro-simulation model. In future, our model will be used to evaluate the cost-effectiveness of individualized treatment strategies.