Cost-effectiveness models of non-small cell lung cancer: A systematic literature review

Michael Willis1, Andreas Nilsson1, Zin Min Thet Lwin1

  • 1The Swedish Institute for Health Economics, Lund, Sweden (Willis, Nilsson, Thet Lwin, Brådvik).

Abstract

Insights

Cost-effectiveness models for non-small cell lung cancer (NSCLC) are increasingly developed, but many lack adherence to reporting standards. Improving model quality and accessibility is crucial for evaluating personalized NSCLC treatments.

Area of Science:

  • Health economics
  • Oncology
  • Biostatistics

Background:

  • Non-small cell lung cancer (NSCLC) poses a significant global health burden.
  • Advances in targeted therapies and immunotherapies offer personalized treatment options.
  • Accurate cost-effectiveness analysis is vital for evaluating NSCLC treatments, especially personalized medicines.

Purpose of the Study:

  • To identify and assess cost-effectiveness models for NSCLC.
  • To evaluate the adherence of these models to reporting standards.

Main Methods:

  • Systematic literature search of PubMed and Embase (2012-2023).
  • Extraction and summarization of study details from identified models.
  • Evaluation of models for adherence to Consolidated Health Economic Evaluation Reporting Standards (CHEERS).

Main Results:

  • 237 unique NSCLC cost-effectiveness models were identified, with 40% published in 2022-2023.
  • Most models utilized standard health states (progression-free survival, progressive disease, death) and time-to-event equations.
  • Thirty models incorporated diagnostic components for biomarker-guided treatment selection.
  • Adherence to CHEERS guidelines was generally incomplete, particularly for model-specific criteria.

Conclusions:

  • The proliferation of NSCLC cost-effectiveness models highlights their importance.
  • Variable adherence to best practices indicates a need for improvement in model quality.
  • Developing high-quality, open-source models could benefit researchers and enhance cost-effectiveness analyses in NSCLC.

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