OBJECTIVES: The validation of complex economic models against observational cohorts tests the accuracy of structural assumptions in models and the relationship between variables and the validity of model outcomes. However, these exercises are seldom conducted and published. The study objectives were to assess how well a newly developed economic model for HIV (ARAMIS-DTG) predicts CD4+ cell-count changes and AIDS-related outcomes and to explore drivers of eventual discrepancies between simulated and observed outcomes. METHODS: Clinical outcomes from ARAMIS-DTG were compared to an observational cohort of patients initiating antiretroviral treatment in the British Columbia Centre for Excellence (BC-CfE) between 2008 and 2011. Baseline population characteristics and treatment efficacy were modified to reflect the observational cohort in the model, and mean changes in CD4+ cell-count and incidence rates of AIDS-related events were simulated in ARAMIS-DTG. The impact of relevant ARAMIS parameters on these outcomes, such as mortality or virological suppression, was assessed in sensitivity analyses. RESULTS: In the base case analysis, the average CD4+ cell-count over five years increased from 295 to 682 cells/µL in ARAMIS-DTG and from 303 to 532 cells/µL in the BC-CfE cohort, reflecting a potential overestimation of adherence in the clinical trial setting. However, despite a higher CD4+ cell-count, the incidence of AIDS-related events was also higher in ARAMIS: 3.81 vs. 2.84 per 100,000 person-years, suggesting the higher incidence of AIDS-related events in ARAMIS-DTG was mostly attributable to the pre-2005 rates used to inform the model. Sensitivity analyses produced similar results without adjusting ARAMIS-DTG to the BC-CfE cohort characteristics. CONCLUSIONS: Although results highlight the need for calibrating economic models and accounting for factors such as adherence to achieve better alignment with real life data, in the particular case of ARAMIS-DTG the differences between simulated and observed outcomes did not significantly affect its overall cost-effectiveness outcomes.