Critical Examination of Modeling Approaches Used in Economic Evaluations of First-Line Treatments for Locally Advanced or Metastatic Non-Small Cell Lung Cancer Harboring Epidermal Growth Factor Receptor Mutations: A Systematic Literature Review.

Abstract

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with up to 32% of patients with NSCLC harboring an epidermal growth factor receptor (EGFR) mutation. NSCLC harboring an EGFR mutation has a dedicated treatment pathway, with EGFR tyrosine kinase inhibitors and platinum-based chemotherapy often being the therapy of choice. The aim of this study was to systemically review and summarize economic models of first-line treatments used for locally advanced or metastatic NSCLC harboring EGFR mutations, as well as to identify areas for improvement for future models. Literature searches were conducted via Ovid in PubMed, MEDLINE, MEDLINE In-Process, Embase, Evidence-Based Medicine Reviews: Health Technology Assessment, Evidence-Based Medicine Reviews: National Health Service Economic Evaluation Database, and EconLit. An initial search was conducted on 19 December 2022 and updated on 11 April 2023. Studies were selected according to predefined criteria using the Population, Intervention, Comparator, Outcome and Study design (PICOS) framework. Sixty-seven articles were included in the review, representing 59 unique studies. The majority of included models were cost-utility analyses (n=52), with the remaining studies being cost-effectiveness analyses (n=4) and a cost-minimization analysis (n=1). Two studies incorporated both a cost-utility and cost-minimization analysis. Although the model structure across studies was consistently reported, justification for this choice was often lacking. Although the reporting of economic models in NSCLC harboring EGFR mutations is generally good, many of these studies lacked sufficient reporting of justification for structural choices, performing extensive sensitivity analyses and validation in economic evaluations. In resolving such gaps, the validity of future models can be increased to guide healthcare decision making in rare indications.