Cost-Effectiveness Analysis in HTA
Also known as: CEA, Cost-Effectiveness Analysis Healthcare
Cost-Effectiveness Analysis (CEA) is an economic evaluation method that compares the cost and health benefits of alternative treatments to determine whether an intervention provides good value for money. Within Health Technology Assessment, CEA is the primary tool for recommending reimbursement and coverage decisions.
Key highlights
- Directly answers the policy question: is the benefit worth the cost given our threshold?
- Enables transparent comparison across diverse technologies and diseases using a common metric (cost per QALY)
- Flexible framework accommodates many types of outcomes and time horizons through modeling
- Sensitivity and scenario analysis quantify uncertainty and support robust decision-making
- Standardized methodology allows benchmarking across countries and institutions
Intuition
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How it works
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When to use it
Use CEA when comparing two or more treatments for the same condition, when time horizons are long (need to project beyond trial data), or when health gains are measured in QALYs rather than monetary terms. CEA is standard for reimbursement decisions in Australia, Canada, the UK, and increasingly in the US. Avoid CEA if outcomes cannot be measured (subjective preferences only), if the technology has no health impact, or if comparing across very different disease areas requires aggregation across non-comparable outcomes.
Strengths & limitations
- Directly answers the policy question: is the benefit worth the cost given our threshold?
- Enables transparent comparison across diverse technologies and diseases using a common metric (cost per QALY)
- Flexible framework accommodates many types of outcomes and time horizons through modeling
- Sensitivity and scenario analysis quantify uncertainty and support robust decision-making
- Standardized methodology allows benchmarking across countries and institutions
- QALY calculation requires utility weights (preference scores) that vary by population and may not capture individual patient values
- Time horizon and discount rate choices significantly affect results; selecting these requires judgment
- Modeling long-term costs and outcomes (e.g., lifetime) introduces substantial uncertainty
- Does not capture equity: a treatment may be cost-effective overall but benefit only wealthy patients
- Thresholds (e.g., $50,000 per QALY) are politically determined and vary across countries; no universal standard
Common pitfalls
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Applications
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Frequently asked
What is the difference between CEA and cost-benefit analysis?
CEA measures benefits in natural units (QALYs, life years). Cost-benefit analysis converts benefits to monetary value (e.g., value of a life = $5 million). CEA is preferred in healthcare because monetizing health is ethically contentious; CEA separates the technical question (cost per outcome) from the value judgment (is this threshold acceptable?).
How do I calculate a QALY?
QALY = (years of life) × (quality weight). Quality weight ranges 0 (death) to 1 (perfect health). A treatment extending life 5 years with quality weight 0.8 = 4 QALYs. Weights come from instruments like EQ-5D (patient self-report) or TTO (time trade-off), eliciting patient preferences.
What discount rate should I use?
Standard is 3% per year for both costs and health benefits (US and many other countries). UK uses 3.5%. The discount rate reflects how much we value future costs and health relative to present: 3% per year means we value a benefit 20 years from now at 55% of its present value.
What if ICER is dominated (more costly and less effective)?
The new treatment should not be funded; the old treatment dominates. If ICER is outside the threshold but provides substantial benefit to a subgroup, consider conditional coverage (e.g., restricted to patients with genetic marker) or requiring further evidence.
How do I handle uncertainty in CEA results?
Use one-way sensitivity analysis (vary one parameter at a time) to identify key drivers. Use probabilistic sensitivity analysis (randomly sample all parameters from distributions) to generate confidence intervals. Create a cost-effectiveness acceptability curve showing probability of cost-effectiveness across thresholds.
Sources
- 1.Gold, M. R., Siegel, J. E., Russell, L. B., & Weinstein, M. C. (Eds.). (1996). Cost-Effectiveness in Health and Medicine. Oxford University Press.ISBN 9780195108231
- 2.Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., & Torrance, G. W. (2015). Methods for the Economic Evaluation of Health Care Programmes (4th ed.). Oxford University Press.
- 3.Shiroiwa, T., Sung-Jae, I., Fukuda, T., & Sanon, M. (2016). International survey on QALYs and cost-effectiveness thresholds. Health Policy, 120(5), 504–514.
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Cite this page
ScholarGate. (2026, June 3). Cost-Effectiveness Analysis in HTA. ScholarGate. https://scholargate.app/healthcare-management/cost-effectiveness-analysis-hta