OBJECTIVES: To assess the cost-effectiveness of an EGFR mutation testing strategy when considering 1st-line therapy of aNSCLC with gefitinib for mutation positive and carboplatin-paclitaxel (CP) for mutation negative disease. METHODS: A Discrete Event Simulation (DES) was designed to emulate two strategies for treating patients with aNSCLC. In the first strategy, patients were tested for EGFR genetic mutation and given gefitinib if positive and CP if negative. In the second strategy patients were not tested for genetic mutation and all of them received CP treatment.  Probabilities for adverse events and progression-free survival (PFS) were obtained from the IPASS clinical study (Mok et al 2009). The mutation rate used was 13% and a sensitivity analysis was run over this variable. A Markov model using micro simulation was also built to compare results of the DES model and assure internal validity.  Both models were run 10 times with 1000 patients for each strategy. Cost-effectiveness ratios were obtained for the testing and not-testing strategies and particularly for positive tested patients treated with gefitinib. RESULTS: Mean PFS (generated by DES) of tested patients with mutation positive disease treated with gefitinib was 11.51 (95% CI, 11.10 – 11.92) months. PFS of patients who where tested for EGFR mutation (positive and negative) was 7.57 (95% CI, 7.50-7.64) months. Patients in the second strategy (without testing) yielded 7.11(95% CI, 7.05 – 7.17) progression-free months. Incremental cost-effectiveness ratio (ICER) of the testing strategy (including test cost) over the not-testing strategy was $1379.49 (95% IC, $1102.10 – $1656.88) per progression-free month. CONCLUSIONS: According to this analysis, testing aNSCLC patients for EGFR mutation is cost-effective with a willingness to pay above $1379.49 per extra progression-free month. In the testing strategy, patients with mutation positive disease treated with gefitinib benefited from an extra 4.52 progression-free months compared to positive patients in the non-testing strategy.