Cost-Effectiveness Analysis in Oncology Treatment Strategies
Summary
Cost-effectiveness analysis has become integral to informing oncology treatment decisions, balancing clinical benefit against economic impact. By quantifying health gains in quality-adjusted life-years (QALYs) and comparing incremental costs, analysts identify interventions that offer optimal value for patients and healthcare systems. Methodologies range from decision-analytic models such as Markov and partitioned survival frameworks to real-world data approaches that capture diverse patient pathways. Key considerations include the selection of appropriate survival curves, utility weightings that reflect quality of life in progressive disease states, and sensitivity analyses to explore uncertainty. Willingness-to-pay thresholds set by national agencies guide interpretation, ensuring that innovations in immunotherapy, targeted agents and precision medicine align with sustainable budgets. Recent advances have focused on stratified analyses by biomarker expression, global burden assessments in low- and middle-income settings, and dynamic pricing negotiations to enhance access. Through rigorous modelling and transparent reporting, cost-effectiveness analysis fosters evidence-based policies that optimise resource allocation and improve patient outcomes across varied tumour types and health systems.
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Cost-Effectiveness Analysis in Oncology Treatment Strategies publication trend
The graph below shows the total number of articles in cost-effectiveness analysis in oncology treatment strategies across all publications each year (not limited to Nature Index journals).
Technical terms
Quality-adjusted life-year (QALY): A composite measure combining life expectancy and health-related quality of life on a scale from 0 (death) to 1 (perfect health).
Incremental cost-effectiveness ratio (ICER): The additional cost per QALY gained when comparing two interventions, guiding value judgments against willingness-to-pay thresholds.
Markov model: A mathematical framework dividing patient trajectories into health states with defined transition probabilities over discrete time cycles.
Partitioned survival model: A modelling approach that allocates patients into progression-free, progressed disease and death states based on survival curves without explicit state transitions.
Willingness-to-pay threshold (WTP): The maximum amount a health system is prepared to spend to gain one additional QALY, often linked to per capita income or policy benchmarks.
References
- Cost-effectiveness of sintilimab plus chemotherapy versus chemotherapy alone as first-line treatment of locally advanced or metastatic oesophageal squamous cell carcinoma. Frontiers in Immunology (2023).
- Cost-effectiveness analysis of PD-1 inhibitors combined with chemotherapy as first-line therapy for advanced esophageal squamous-cell carcinoma in China. Frontiers in Pharmacology (2023).
- Cost-effectiveness analysis of adjuvant chemotherapies in patients presenting with gastric cancer after D2 gastrectomy. BMC Cancer (2014).
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