A Systematic Review of Health Economic Evaluation on Targeted Therapies for First-Line Treatment of Metastatic Non-Small Cell Lung Cancer (NSCLC): Quality Evaluation

BACKGROUND: Evolving practices in non-small cell lung cancer (NSCLC) therapy inevitably affect health care budgets, especially through the introduction of targeted therapies. This results in a rise of health economic evaluations (HEEs) in this domain. This article reviews the quality of the economic evidence of targeted therapies used in metastatic NSCLC. METHODS: A literature search was conducted using PubMed, Cochrane, Embase and CRD (University of York Centre for Reviews and Dissemination) databases to identify topical original articles published between 1/1/2000 and 3/31/2019. A quality of reporting assessment using the CHEERS (Consolidated Health Economic Evaluation Reporting Standards statement) checklist was converted into a quantitative score and compared with the results of a QHES (Quality of Health Economic Studies) evaluation. Components of QHES were also used to analyze the validity of primary outcomes, consideration of heterogeneity and rationality of main assumptions of models in modeling studies. RESULTS: In total, 25 HEEs were obtained and analyzed. From the CHEERS assessment, it was found that method description integrity (including setting, perspective, time horizon and discount rate), justification of data sources and a heterogeneity description were often absent or incomplete. Only five examined studies met the accepted standard of good quality. Modeled articles were examined with the QHES instrument, and a lack of illustrated structure, population variability, formula of the transitioning probability and justification for the choice of the model were the most frequently observed problems in the selected studies. After quantification, the CHEERS scores and QHES scores did not differ significantly. CONCLUSION: Current NSCLC models generally lack consideration for demographic heterogeneity and transparency of data description, and it would be difficult to transfer or generalize from the scientific literature to real-world evidence-based decision-making. Frameworks of future models should be informed and justified based on the validity of model results and the improvement of modeling accuracy.