Dominance

Dominance in Cost-Effectiveness Analysis

Dominance is the general rule for ruling out an option in cost-effectiveness analysis when another option, or a blend of two others, is no worse on cost or effect and better on one. It takes three forms. Under strict dominance, another option is both cheaper and more effective. The weak dominance form also covers ties on cost or on effect. Under extended dominance, a blend of a cheaper and a more expensive option gives at least the same effect for less. The pairwise forms, strict and weak, can be read directly from expected costs and effects, whereas extended dominance only appears in a fully incremental analysis, so the two need to be kept apart when ICERs are calculated.

A dominance finding can settle a cost-effectiveness comparison without applying a threshold. However, dominance does not by itself establish affordability, feasibility or the final institutional decision.

What dominance means

In the pairwise case, an intervention dominates its comparator when it costs no more, produces no less health and is strictly better in cost, health outcome or both. The comparator is described as dominated because no cost-effectiveness trade-off justifies choosing it under the stated evidence.

Dominance is based on expected comparative costs and outcomes. It is not a claim that an intervention is clinically superior on every outcome or preferable for every patient.

• A dominant intervention is no more costly and no less effective than its comparator, with at least one strict improvement.
• A dominated intervention is no less costly and no more effective than its comparator, with at least one strict disadvantage.
• A dominance finding removes the need to compare an ICER with a cost-effectiveness threshold.
• A dominance finding should identify the comparator, population, perspective, time horizon and outcome measure.

How dominance is identified

Dominance is assessed using incremental cost and incremental effectiveness. Both differences should use the evaluated intervention minus the comparator so that their signs have a consistent meaning.

Let ΔC represent incremental cost and ΔE represent incremental effectiveness. The two differences are calculated as follows.

ΔC = Cost of intervention − Cost of comparator

ΔE = Effect of intervention − Effect of comparator

The combination of signs for cost and effect determines the interpretation, as the table shows.

Equal values also require careful treatment. An intervention dominates when it has equal cost and greater effectiveness, or equal effectiveness and lower cost; the reverse comparisons are dominated.

A worked dominance example

Suppose a new treatment costs £7,500 per patient and produces 4.8 QALYs, while current care costs £10,000 and produces 4.2 QALYs. The new treatment is both less costly and more effective.

Its incremental cost is −£2,500 and its incremental effectiveness is +0.6 QALYs. The new treatment therefore dominates current care, and an ICER threshold comparison is unnecessary.

ΔC = £7,500 − £10,000 = −£2,500

ΔE = 4.8 − 4.2 = +0.6 QALYs

• The example uses synthetic values and does not describe a real treatment or reimbursement decision.
• The dominance conclusion depends on the stated comparator and analytical assumptions.
• The result does not establish the total budget impact of adopting the treatment.

Why a negative ICER is ambiguous

A negative ICER occurs when incremental cost and incremental effectiveness have opposite signs. That can represent either a dominant intervention or a dominated intervention.

The ratio’s sign does not reveal which underlying difference is negative. Analysts should therefore inspect ΔC and ΔE separately rather than reporting or interpreting a negative ICER as though it were an ordinary cost-per-QALY result.

• Negative ΔC and positive ΔE indicate that the intervention dominates the comparator.
• Positive ΔC and negative ΔE indicate that the intervention is dominated by the comparator.
• A negative ICER should not be compared mechanically with a positive cost-effectiveness threshold.
• Net-benefit methods provide an interpretable result across both negative-ICER quadrants.

How dominance differs from a trade-off

Dominance between two options occurs only when one option is at least as good on both cost and health outcome and strictly better on at least one. When one option improves health but increases cost, or reduces cost but sacrifices health, the comparison involves a trade-off.

Trade-off comparisons require a decision rule that values additional health relative to additional cost or savings. An ICER or net-benefit calculation may then be appropriate after the cost-effectiveness-plane quadrant has been identified.

• A more effective and more costly intervention requires a cost-versus-health trade-off.
• A less effective and less costly intervention requires a savings-versus-health trade-off.
• A less costly and more effective intervention dominates its comparator.
• A more costly and less effective intervention is dominated by its comparator.

How dominance is used with several alternatives

When several mutually exclusive alternatives are compared, dominance must be assessed before sequential ICERs are interpreted. An option that is dominated cannot lie on the efficient cost-effectiveness frontier and should not remain in the incremental comparison.

After strictly or weakly dominated options are removed, the remaining alternatives are ordered by expected cost and compared sequentially. Extended dominance is then assessed before the final ICERs are calculated.

1. Order the alternatives from lowest to highest expected cost.
2. Compare each alternative’s expected cost and expected effectiveness with the other options.
3. Remove alternatives that are strictly or weakly dominated.
4. Calculate sequential incremental costs, effects and ICERs for the remaining alternatives.
5. Remove alternatives subject to extended dominance.
6. Recalculate the sequential ICERs after every removal.
7. Apply the cost-effectiveness threshold to the final efficient frontier.

How pairwise dominance differs from extended dominance

Pairwise (strict or weak) dominance is a direct comparison, in which another available option costs no more and produces no less health, with at least one strict improvement. No combination or sequence of alternatives is required to identify it.

Extended dominance occurs when an alternative is less efficient than a combination of other options. It is identified through fully incremental analysis when sequential ICERs fail to increase as effectiveness rises.

• Pairwise dominance is visible from the expected cost and expected effectiveness of individual alternatives.
• Extended dominance depends on the relative efficiency of multiple alternatives.
• A strictly or weakly dominated option is removed before calculating the final sequential ICERs.
• An option subject to extended dominance is removed before constructing the final cost-effectiveness frontier.
• Removing either type of dominated option requires the incremental analysis to be recalculated.

Why dominance should be checked before calculating ICERs

Including a dominated option in an incremental analysis can produce misleading ratios and an incorrect cost-effectiveness frontier. Dominance screening ensures that every reported sequential ICER compares relevant efficient alternatives.

A pairwise ICER against a common baseline is not sufficient when several mutually exclusive options exist. The order and removal process must be visible so that the final comparisons can be reproduced.

• Dominance checks should precede threshold-based ICER interpretation.
• Dominated options should not be used as the next comparator on the final efficiency frontier.
• Sequential comparisons should be recalculated after each removal.
• The analysis should retain an audit trail showing why each option was excluded.

How uncertainty affects a dominance finding

A base-case analysis may classify an intervention as dominant even when costs and outcomes are uncertain. Probabilistic results may show that sampled comparisons fall in several quadrants of the cost-effectiveness plane.

Expected-value decision-making should use expected net benefit rather than the percentage of simulations in which an option is dominant. The probability of dominance can describe uncertainty, but it is not a complete decision rule.

• Base-case dominance describes the comparison using expected or central estimates.
• Probabilistic analysis can show how often the comparison falls within each cost-effectiveness-plane quadrant.
• A high probability of dominance does not replace expected-net-benefit comparison.
• Parameter and structural uncertainty should accompany the dominance conclusion when they could change the preferred option.

How to check dominance in Excel

Excel can classify two-option comparisons when the worksheet clearly separates costs, effects and incremental results. The formulas should recognise equality boundaries as well as comparisons in which both values differ.

Suppose incremental cost is in D2 and incremental effectiveness is in E2. The formula below classifies the comparison as dominant, dominated, one of the two trade-off situations or a tie.

=IF(AND(D2<=0,E2>=0,OR(D2<0,E2>0)),"Dominant",IF(AND(D2>=0,E2<=0,OR(D2>0,E2<0)),"Dominated",IF(AND(D2>0,E2>0),"More costly and more effective",IF(AND(D2<0,E2<0),"Less costly and less effective","Tie"))))

Simpler formulas can support the calculation.

• Use =B2-B3 to calculate incremental cost when B2 contains intervention cost and B3 contains comparator cost.
• Use =C2-C3 to calculate incremental effectiveness when C2 contains intervention effectiveness and C3 contains comparator effectiveness.
• Use =FILTER(A2:F20,F2:F20<>"Dominated") to display options not classified as dominated, while retaining the complete source table for audit.
• Recalculate sequential comparisons after any dominated alternative is removed.

Common mistakes and safeguards

Dominance errors often result from interpreting the ICER before inspecting its components or from comparing alternatives inconsistently. These mistakes can retain inefficient options or reverse the meaning of a negative ratio.

The safeguard is to report expected costs and health outcomes alongside incremental differences and the cost-effectiveness-plane quadrant. Multi-option analyses should document every removal and recalculation.

• Interpreting every negative ICER as favourable confuses dominance with being dominated.
• Requiring both cost and effectiveness to differ can miss dominance at equality boundaries.
• Describing clinical superiority alone as economic dominance ignores comparative cost.
• Comparing options with different populations, perspectives or time horizons invalidates the dominance assessment.
• Leaving a dominated option in a multi-option analysis can distort sequential ICERs.
• Confusing pairwise dominance with extended dominance obscures why an option was removed.
• Selecting the option most often dominant in probabilistic simulations can differ from selecting the option with the greatest expected net benefit.
• Treating dominance as proof of affordability confuses efficiency with total financial impact.

What should be reported

A transparent dominance assessment should allow readers to verify the direction of the comparison and reproduce the classification. Reporting only that an intervention is dominant or dominated is insufficient.

Reporting the items below makes the finding auditable and prevents the term from being interpreted more broadly than the evidence supports.

• Report the evaluated intervention and comparator.
• Report the population, perspective, time horizon and health-outcome measure.
• Report expected costs and expected health outcomes for each alternative.
• Report incremental cost and incremental effectiveness with a consistent subtraction direction.
• Report the cost-effectiveness-plane quadrant.
• Report whether the result is strict, weak or extended dominance, or a trade-off.
• Report every option removed from a multi-option analysis and the reason for removal.
• Report the recalculated sequential comparisons after each removal.
• Report uncertainty that could change the dominance classification.
• Report budget impact and wider HTA considerations separately from the dominance result.

Sources

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• Barton GR, Briggs AH, Fenwick EA. Optimal cost-effectiveness decisions: the role of the cost-effectiveness acceptability curve (CEAC), the cost-effectiveness acceptability frontier (CEAF), and the expected value of perfection information (EVPI). Value in Health. 2008;11(5):886-897. doi:10.1111/j.1524-4733.2008.00358.x

• Cantor SB. Cost-effectiveness analysis, extended dominance, and ethics: a quantitative assessment. Medical Decision Making. 1994;14(3):259-265. doi:10.1177/0272989X9401400308

• Drummond MF, Sculpher MJ, Claxton K, Stoddart GL, Torrance GW. Methods for the Economic Evaluation of Health Care Programmes. 4th ed. Oxford: Oxford University Press; 2015.

• Health Economics Resource Center (HERC), US Department of Veterans Affairs. Cost-effectiveness analysis. Last updated 10 June 2026. https://www.herc.research.va.gov/include/page.asp?id=cost-effectiveness-analysis

• National Institute for Health and Care Excellence. NICE technology appraisal and highly specialised technologies guidance: the manual (PMG36). London: NICE; 2022, last updated 13 October 2026. Sections 4.2.16, 4.7.15 and 4.10.8.

• York Health Economics Consortium. Dominance. Glossary entry. Published October 2016, last updated October 2025. https://yhec.co.uk/glossary/dominance/