OBJECTIVES: Gene therapy (GTx) has emerged as a promising approach for curing life-threatening diseases, but its high cost poses economic challenges. This study aims to review and summarize the current state of cost-effectiveness models (CEMs) in GTx research, highlighting the prevalent modeling approaches and challenges.

METHODS: A targeted literature review was conducted in PubMed to gather relevant evidence on CEMs of GTx in recent years, spanning from January-2018 to May-2023. Only full-text papers published in English were included in the review process.

RESULTS: A review of 375 citations revealed 28 CEMs. The majority of CEMs (67% [n=20]) were conducted for the US setting. Among the identified models, 25 (89%) utilized a cohort-level approach, while three (11%) employed a patient-level approach. Markov model (46% [n=13]) was the most commonly used approach for patient simulation, followed by partitioned survival model (32% [n=9]), discrete event simulation (11% [n=3]), and hybrid model combining the decision tree and Markov model (11% [n=3]). Patients were simulated until lifetime in most models (89% [n=25]). Most models used Payer’s perspective, except three models which considered societal perspective. Among the 39 comparisons made between GTx and standard care, GTx was found to be cost-effective (56% [n=22]) or dominant (18% [n=7]) in most cases, with 10 (26%) comparisons indicating non-cost effectiveness. The most influential parameters across all CEMs were related to price of GTx, effectiveness durability of GTx, and utility values. Several key challenges in CEMs were identified, such as usage of single-arm clinical trials, assumptions around treatment durability, long-term extrapolations, utility estimates, and insufficient real-world evidence.

CONCLUSIONS: Further research, with a specific focus on extrapolation and matched comparisons to historical cohorts, is warranted to explore novel methodological approaches to address the modeling challenges identified in this study and enhance the accuracy and reliability of cost-effectiveness evaluations in the context of GTx.