OBJECTIVES For the evaluation of the potential cost-effectiveness of an early experimental therapy, we calibrated an existing micro-simulation model for radiotherapy planning in lung cancer using pilot data. METHODS We used an externally validated micro-simulation model, build using Real World Evidence data. The model contained four clinical states from alive to death, with intermediate states ‘local recurrence’ and ‘metastasis’, with 5 transitions. Based on individual and time-dependent hazard rates, patients move through the model according to their combination of patient characteristics and random variation. For the experimental dosis-escalation therapy we had limited pilot study data, which included overall survival and a number of baseline characteristics. The distribution of patient features in the cohort of the micro-simulation model was adjusted so that the simulated patients had the same baseline characteristics as the patients that received experimental therapy. Alternative radiotherapy strategies affected 5 transitions in the model, quantified by 5 hazard ratios (HRs). Subsequently, HRs for experimental radiotherapy compared to current radiotherapy were calibrated until they were able to satisfactorily reproduce the survival curve of the pilot data. The best fitting sets of HRs were selected based on the least Sum of Squared Errors (SSE) of the model predictions and the survival curve of the experimental therapy on three time points. RESULTS The best fitting set HRs resulted in a SSE of 0,005 based on prediction errors at 1, 2 and 3-year survival. Although 33 out of 1000 sets produced predictions with less than 5% prediction error, hazard ratios varied strongly within and over the different sets. CONCLUSIONS By using calibration, we obtained a micro-simulation model that is suitable for the evaluation of new treatments in the absence of empirical data. The model will be used for cost-effectiveness analyses, where the variation in hazard ratios within sets will be evaluated in scenario analyses.