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Objective: Assess the cost-effectiveness of an EGFR-mutation testing strategy for advanced NSCLC in first-line therapy with either gefitinib or carboplatin-paclitaxel in Mexican institutions.Methods: Cost-effectiveness analysis using a discrete event simulation (DES) model to simulate two therapeutic strategies in patients with advanced NSCLC. Strategy one included patients tested for EGFR-mutation and therapy given accordingly. Strategy two included chemotherapy for all patients without testing. All results are presented in 2014 US dollars. The analysis was made with data from the Mexican frequency of EGFR-mutation. A univariate sensitivity analysis was conducted on EGFR prevalence. Progression-free survival (PFS) transition probabilities were estimated on data from the IPASS and simulated with a Weibull distribution, run with parallel trials to calculate a probabilistic sensitivity analysis.Results: PFS of patients in the testing strategy was 6.76 months (95 % CI 6.10-7.44) vs 5.85 months (95 % CI 5.43-6.29) in the non-testing group. The one-way sensitivity analysis showed that PFS has a direct relationship with EGFR-mutation prevalence, while the ICER and testing cost have an inverse relationship with EGFR-mutation prevalence. The probabilistic sensitivity analysis showed that all iterations had incremental costs and incremental PFS for strategy 1 in comparison with strategy 2.Conclusion: There is a direct relationship between the ICER and the cost of EGFR testing, with an inverse relationship with the prevalence of EGFR-mutation. When prevalence is >10 % ICER remains constant. This study could impact Mexican and Latin American health policies regarding mutation detection testing and treatment for advanced NSCLC. [ABSTRACT FROM AUTHOR]

Problem: The difficulty to measure the IC in Latin American universities highlights its little interest even though its relevance to the knowledge economy. Purpose: Design and validate a survey for measure Intellectual Capital in universities from a Resource-based Theory perspective and a three components model (human capital, structural capital and relational capital). Method: A subject matter expert panel technique was used to generate the items. It was empirically tested with a convenience sample (n=742) of academics, middle managers and principals of institutions of higher education in México. Results and Conclusions: With exploratory factor analysis and principal components analysis methods, four factors were detected (human capital, structural capital, relational capital and technological capital) explaining total variance of 66.74%. All subscales obtained Cronbach’s Alpha higher to 0.80. There were advances on the measure quality and stability of the questionnaire. In order to generate measure and structural models this study could be replicated. [ABSTRACT FROM AUTHOR]