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Economic evaluationQALYDALYHTAHealth technology assessmentUtility value

Cost-Utility Analysis

Cost-Utility Analysis
Also known as: CUA、Cost-utility analysis

An economic evaluation that combines health-related quality of life and survival in measures such as QALYs and compares them with costs. Align scales, valuation, periods and comparators, and examine additional cost per QALY. [1]

Cost-utility analysis (CUA) design checklist — openReferences and checked sections
Difficulty
Advanced
Cost
High
Duration
Planning example, not a universal standard duration: 3–12 months when utility measurement and a systematic literature review are included
Content updated: 2026-09-13Source checked: 2026-09-13 (includes AI cross-checking)No documented human reviewIncludes fictional examples

How it works at a glance

QALYs sum the time in each health state multiplied by its utility value. Utility values depend on the scale and valuation method and differ from simple satisfaction scores. [1]

Calculate ICER by dividing incremental costs by incremental QALYs. Check the analytical perspective, discounting and adoption criteria for each system, and do not confuse valuing health states with valuing individuals. [1]

  1. Define the analytical framework
  2. Collect cost data
  3. Identify utility values
  4. Calculate QALYs
  5. Calculate ICER
  6. Analyze uncertainty
  7. Assess thresholds and interpret context
Sequence of implementation steps. See “Implementation steps” for details of each stage.

When to use it and when it is unsuitable

When to use it

  • When evaluating cost-effectiveness for listing and pricing new medicines, devices or technologies under an official pricing system (HTA: health technology assessment)
  • When evaluating interventions with multiple health outcome dimensions, such as longer survival and better quality of life, and combining them into QALYs
  • When comparing interventions across disease areas using a common measure, QALYs or DALYs, to prioritize budget allocations
  • When an economic evaluation report must comply with international guidelines, such as NICE or HTA guidance

When it is unsuitable

  • When non-health benefits are central to a decision and QALYs cannot represent important values. Educational or environmental interventions may still be evaluated for health impacts.
  • For rare or emerging diseases without sufficient utility measurement data. Estimating utility using proxy values reduces analytical reliability.
  • When health inequalities or justice are central. Maximizing QALYs prioritizes aggregate efficiency and does not incorporate distributive justice.

Participants, roles, and deliverables

Participants and roles

Health economist leading analysis, clinical specialists reviewing disease natural history and clinical effects, statistician responsible for PSA, and regulatory staff checking HTA guideline compliance.

Main deliverables

  • Estimated costs and QALYs for intervention and control groups
  • ICER calculations and cost-effectiveness plane
  • Probabilistic sensitivity analysis (PSA) results and CEAC
  • CUA report, including an HTA submission format

Implementation steps

  1. Define the analytical framework
    Specify the disease, intervention, comparator, perspective—health insurer or societal—and time horizon, such as a lifetime cohort. Follow HTA guidelines where available.
  2. Collect cost data
    Collect medical costs, including medicines, inpatient and outpatient care, patients' direct non-medical costs and indirect costs such as lost work, according to the perspective. Include future costs and apply discounting.
  3. Identify utility values
    Obtain utility values for each health state from existing studies using standard measures such as EQ-5D-5L, or collect new measurements. Specify the tariff used, such as the Japanese or UK tariff.
  4. Calculate QALYs
    Multiply each health state's utility by time spent in it to estimate QALYs for intervention and control groups. State whether the analysis is model-based, such as a Markov model, or uses individual data.
  5. Calculate ICER
    ICER = (intervention cost − control cost) ÷ (intervention QALYs − control QALYs). Use discounted costs and QALYs. Plot ICER on the cost-effectiveness plane.
  6. Analyze uncertainty
    Use sensitivity analysis varying values, or probabilistic sensitivity analysis, according to the model and purpose. For probabilistic analysis, explain distributions and dependencies between parameters.
  7. Assess thresholds and interpret context
    Check current guidance applicable to the recipient and decision-making system, and consider other benefits, costs, distribution and uncertainty alongside ICER. This explanation does not set a particular system's adoption threshold as a universal standard.

Strengths and limitations

Strengths

  • The single measure of QALYs enables comparisons across diseases and interventions and prioritization across the health care budget.
  • Combines health quality with quantity, years of survival, preventing evaluation based only on life extension.
  • Organizes health benefits in common QALY units. Check guidance for the relevant country, system and purpose to determine submission compliance.
  • Probabilistic sensitivity analysis (PSA) systematically quantifies uncertainty.

Limitations

  • QALYs can vary substantially with the scale, such as EQ-5D or SF-6D, and tariff, country-specific preference weights, raising reproducibility issues.
  • Total QALYs cannot fully represent the distribution or equity of health benefits; supplement decisions with impacts on target groups.
  • Rare diseases have limited effect and utility data, creating substantial extrapolation and estimation uncertainty.
  • Measuring health states and valuing them are distinct processes. Specify whose responses and preferences produced the utility values.

Common pitfalls

  • Underestimating the importance of tariff selection. Applying UK versus Japanese tariffs to identical patient data can substantially change ICER. Always check the tariff required by the receiving regulator.
  • Check whether the time horizon omits important future costs and benefits. Do not automatically select a lifetime horizon: justify a period capturing differences between interventions and any extrapolation.
  • Do not make adoption decisions from a single ICER. Analyze uncertainty in values, models and time horizons, and explain methods required by applicable guidance and remaining limitations.

Conditions of use and evidence

  • QALYs combine time and health-related utility. Compare them with attention to differences in scales and valuation. [1]
  • Do not uniformly restrict utility values to 0–1. EQ-5D-5L value sets can include negative values, but these express preferences for health states, not individuals' worth. [2]
Fictional example; not actual results

Check the formula using one year of health states

A fictional illustrative calculation per person, assuming everyone survives 1 year and utility is constant throughout the year. It does not recommend an actual treatment.

OptionCostUtility × yearsQALY
AJPY 100,0000.6×10.6
BJPY 120,0000.7×10.7
B relative to AJPY 20,000 increase0.1 QALY increaseICER = JPY 200,000 per QALY

Common errors in judgment

Common mistake

Converting a satisfaction score of 70 to utility 0.7

How to revise

Use validated measures and valuation appropriate to the relevant system.

ReasonSatisfaction scores and health utility are not the same scale.

Common mistake

Assuming a low ICER also means a small budget impact

How to revise

Estimate budget impact separately, including the target population size.

ReasonCost-effectiveness per unit differs from total expenditure.

Organize your thinking by writing

Cost-utility analysis (CUA) design checklist

A practical template is available to fill in and print on A4 paper.

Open worksheet

Related methods

Cost-Effectiveness AnalysisCost-Benefit AnalysisMeta-Analysis and Systematic ReviewRandomized Controlled TrialValue for Money

References and checked sections

  1. NICE technology appraisal and highly specialised technologies guidance: Economic evaluation
    National Institute for Health and Care Excellence
    Section consulted
    4.2.14–4.2.16 / 4.3.1–4.3.5
    How it relates to the text
    QALYs combine time and health-related utility. Compare them with attention to differences in scales and valuation.
    Source checked: (includes AI cross-checking)
  2. eq5d5l: A command to estimate preference-based values（EuroQol Working Paper Series 19001）
    Juan M. Ramos-Goñi／Jesús Oramas-Zarate／Oliver Rivero-Arias · 2019
    Section consulted
    1 Description, printed p.2
    How it relates to the text
    Utility value ranges and cautions in the example about the original form's 0–1 notation
    Source checked: (includes AI cross-checking)

Editorial and checking information

Content updated: Source checked: (includes AI cross-checking)No documented human reviewIncludes fictional examplesEditorial policy and scope of checkingCite this pageReport a correction

How to cite this page

EvalCompass Editorial Team. “Cost-Utility Analysis”. EvalCompass, updated 2026-09-13. https://evalcompass.jun-nakatani.com/en/methods/cua/
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Change history
  • — Checked sources for definitions and applicability conditions, and added fictional examples, reasons for judgments and examples for every worksheet field. Revised unverified claims about history and adoption. Also reviewed existing procedures and cautions, and adjusted fixed participant counts and thresholds and generalizations about institutions. Preserved field positions, types and counts while revising fixed conditions and thresholds in the forms.