OBJECTIVES: The ISPOR-SMDM Modeling Good Research Practices Task Force highlights the importance of model transparency and validation, including cross validation, as a means to establishing trust and confidence in economic models. (1) To investigate how a technical model validation can be conducted by reconstructing a previously-developed Microsoft Excel cost-effectiveness model using TreeAge; and, (2) to validate the model by comparing the results of two implementations. METHODS:  An Excel-based cohort model on indolent NHL was reconstructed in in TreeAge using the same model structure, clinical inputs, and costing assumptions.  Lifetime costs, life-years, quality-adjusted life-years, and incremental cost-effectiveness ratios (ICERs) were projected and compared for bendamustine-rituximab (Ben-R) versus fludarabine-rituximab (Fdb-R) in relapsed indolent NHL patients in Colombia.  All costs were in 2013 Colombian pesos. The base-case results and sensitivity analyses were compared between the two software tools and key implementation considerations were identified. RESULTS:  Key differences in the two software tools were identified and implementation differences will be described, including handling of survival inputs and application of one-time and per-cycle costs.  The TreeAge model produced more favorable results compared to the Excel model.  The total costs for Ben-R and Fdb-R were $223,400,660 and $208,115,352 in the TreeAge model, respectively, while in the Excel model they were $291,192,912 and $260,463,392.  The ICERs were $11,582,974/LY and $13,815,417/QALY in the TreeAge model and $23,286,360/LY and $27,956,124/QALY in the Excel model.  However, once the differences between the two models were accounted for in implementation, the reconstructed TreeAge model produced approximately the same results compared to the original Excel model (23,381,795/LY vs. $23,286,360/LY). CONCLUSIONS: There are inherent differences in model implementation in Excel vs. TreeAge that should be considered when performing double implementation and when interpreting the model results. Model validation using two software tools is a practical way to ensure proper and intended implementation.