Comparator

Why the comparator matters

Economic evaluation compares the costs and outcomes of alternative courses of action rather than examining an intervention in isolation. The comparator establishes the reference point for that comparison, so changing the comparator can change the incremental costs, incremental outcomes, cost-effectiveness ratio and net benefit of the intervention.

This page explains how to select a decision-relevant comparator, distinguish it from a clinical-trial control, handle multiple or mixed alternatives, and report the choice transparently. It also shows how comparator selection affects economic results even when the intervention itself remains unchanged.

How to select a relevant comparator

A comparator should represent a feasible alternative for the population, setting and decision under consideration. It should reflect what would realistically happen if the evaluated intervention were not adopted, rather than simply using whichever control happened to be included in a clinical trial.

A structured selection process helps prevent relevant alternatives from being overlooked. The process should be completed before the economic model and evidence strategy are finalised.

1. Define the population, decision-maker and healthcare setting.
2. Describe the current clinical or service pathway.
3. Identify all feasible alternatives available to the population.
4. Determine what the intervention would replace, supplement or displace.
5. Examine whether current practice varies across subgroups, providers or jurisdictions.
6. Identify emerging alternatives likely to affect the decision.
7. Compare the decision-relevant alternatives with the controls used in the clinical evidence.
8. Include every relevant mutually exclusive option in the economic analysis.
9. Define any composite or treatment-mix comparator transparently.
10. Test plausible alternative comparators when the base-case choice is uncertain.

Comparator selection should reflect actual or expected practice, not only what a guideline states should happen. Administrative data, registries, prescribing records, clinical audits, treatment pathways and expert elicitation can help establish the relevant alternatives.

Common types of comparator

Different decision problems require different types of comparator. Labels such as “usual care,” “standard management” and “no treatment” are not sufficiently precise unless the services, treatments and resource use included within them are described.

The following table summarises common comparator types and the clarification each one requires.

Comparator type	When it may be appropriate	What must be clarified
Current practice	Existing care represents the option likely to continue without adoption	The actual treatment pathway, utilisation and practice date
Active comparator	Another available treatment addresses the same decision problem	Its availability, clinical role and relevance to the population
Standard of care	A recognised routine management approach provides the reference option	The specific components of the standard-care pathway
Best supportive care	Supportive management is provided without the evaluated active treatment	The included services, symptom management and background care
No intervention	No active programme or treatment is a feasible option	Any background care, monitoring and natural disease progression
Placebo	A controlled comparison is needed to estimate treatment efficacy	Whether placebo represents a realistic decision alternative
Treatment mix	Several treatments collectively represent current practice	The component treatments, weights, data sources and time period
Add-on care	The intervention supplements rather than replaces existing management	The background care received by both comparison groups
Near-market alternative	An emerging technology could soon affect the decision	Its expected availability and role in base-case or scenario analysis

An active comparator may be appropriate when an established treatment already addresses the same clinical need. A no-intervention comparator may be appropriate only when withholding the active programme is a genuine option and any continuing background care is still represented.

Why a clinical-trial control may not be the right comparator

The control arm of a clinical trial answers the trial’s research question, but it does not automatically represent the alternative relevant to an economic decision. A placebo-controlled trial may establish efficacy even though the practical decision concerns whether a new treatment should replace an existing active treatment.

The reverse problem can also occur. A head-to-head trial may use an active control that is unavailable, rarely used or inappropriate in the jurisdiction where the economic evaluation will inform a decision.

Aligning the clinical evidence with the economic comparator may require additional analytical work. Each adjustment introduces assumptions that should be documented and tested.

• Direct head-to-head evidence can estimate relative effects against the required comparator.
• Network meta-analysis can compare several treatments through a connected evidence network.
• Indirect treatment comparison can estimate relative effects when direct evidence is unavailable.
• Population adjustment can address important differences between trial populations.
• Observational evidence can describe local treatment patterns, outcomes or baseline risks.
• Model calibration can align simulated outcomes with relevant external evidence.
• Clinical-pathway modelling can represent an alternative not observed directly in the trial.

The economic evaluation should clearly distinguish the clinical-evidence control from the decision comparator. Any method used to connect them should be reported with its assumptions and uncertainty.

How current practice can vary

Current practice is not always a single, stable treatment. It may differ between patient groups, providers, geographical areas, healthcare sectors or stages of a treatment pathway.

The evaluation should investigate material variation instead of assuming that one national or historical average represents every decision setting. The following factors commonly affect the definition of current practice.

• Current practice may differ according to disease severity or previous treatment.
• Current practice may differ between hospitals, clinics or geographical areas.
• Current practice may be restricted by eligibility rules, capacity or access.
• Current practice may involve several treatments used in different proportions.
• Current practice may change following new guidance, price changes or market entry.
• Current practice may include background care even when no active treatment is provided.

The evidence used to define current practice should be recent enough for the decision. Historical market shares should not be treated as current practice without checking whether the pathway has changed.

How to compare several alternatives

Some decisions involve more than one relevant comparator. When alternatives are mutually exclusive, a fully incremental analysis should compare all feasible options together rather than comparing every intervention separately with one fixed baseline.

The alternatives are ordered by expected outcome before dominance and incremental results are assessed. This process identifies the efficient options that form the cost-effectiveness frontier.

1. Order the alternatives from the least effective to the most effective.
2. Remove any strictly dominated alternative that costs more and produces fewer outcomes than another option.
3. Calculate incremental costs and outcomes between adjacent alternatives.
4. Identify alternatives subject to extended dominance.
5. Remove the extendedly dominated alternatives.
6. Recalculate the incremental sequence after each exclusion.
7. Construct the cost-effectiveness frontier from the remaining alternatives.
8. Compare the remaining options using the applicable threshold or net-benefit rule.

A collection of pairwise comparisons against a common baseline can select an inefficient option because it does not show the additional cost and outcome of moving between adjacent alternatives. The fully incremental sequence should therefore be reported whenever all included options represent competing choices.

When a treatment-mix comparator is appropriate

A treatment mix can represent current practice when several alternatives are used across the target population and the decision concerns replacing that combined pattern of care. The component treatments and their weights must remain visible so that variation is not hidden inside a single average.

The expected cost and outcome of a mixed comparator are calculated as weighted averages. The weights should describe the target population, setting and relevant practice period and must sum to one.

Mixed comparator cost = Σ(wⱼ × Cⱼ)

Mixed comparator outcome = Σ(wⱼ × Eⱼ)

In these equations, wⱼ is the proportion receiving comparator j, Cⱼ is its expected cost, and Eⱼ is its expected outcome. The condition Σwⱼ = 1 confirms that the treatment shares represent the entire comparator population.

A weighted treatment mix is inappropriate when its components are mutually exclusive policy choices that should be compared separately. Sensitivity analysis should be used when the component weights are uncertain or changing.

Replacement, add-on treatment and displacement

Adoption does not always produce a simple one-for-one replacement of an existing treatment. A new intervention may supplement background care, displace several treatments, alter later treatment sequences or expand treatment to people who previously received no active intervention.

The comparator should reflect the change expected in practice. The initial adoption decision should also be distinguished from later switching, rescue treatment and subsequent-line care represented within the model.

• A replacement intervention should be compared with the treatment or treatments it is expected to displace.
• An add-on intervention should usually be compared as the intervention plus background care versus background care alone.
• A pathway-changing intervention should include relevant changes to subsequent tests, treatments and services.
• An intervention that expands eligibility should include the outcomes and costs of the newly treated population.
• An intervention that changes the care setting should represent displaced resources in both the original and new settings.

Comparing an add-on treatment with no care can incorrectly attribute the effects of background management to the new intervention. The model should therefore maintain consistent background care across the relevant comparison groups.

How the comparator changes the result

The same intervention can produce different incremental results when evaluated against different alternatives. This does not mean that the intervention’s own costs or outcomes have changed; it means that the reference decision has changed.

The following example compares one intervention with current practice and an active alternative.

Option	Expected cost	Expected QALYs
New intervention	£18,000	4.50
Current practice	£12,000	4.20
Active alternative	£14,000	4.40

When the new intervention is compared with current practice:

Incremental cost = £18,000 − £12,000 = £6,000

Incremental QALYs = 4.50 − 4.20 = 0.30

ICER = £6,000 ÷ 0.30 = £20,000 per QALY

When the same intervention is compared with the active alternative:

Incremental cost = £18,000 − £14,000 = £4,000

Incremental QALYs = 4.50 − 4.40 = 0.10

ICER = £4,000 ÷ 0.10 = £40,000 per QALY

The intervention appears less cost-effective against the active alternative because that comparator already produces more QALYs than current practice. These isolated calculations still do not replace a fully incremental analysis when all three options are mutually exclusive.

Keeping the comparison consistent

The comparator must be represented consistently throughout the economic evaluation. Misalignment between the stated comparator, clinical evidence, model structure, resource use and costs can make the incremental result difficult to interpret.

Consistency should be checked across every part of the analysis. Differences should be adjusted or clearly explained rather than left implicit.

• The decision problem should name the same comparator represented in the model.
• The clinical evidence should support the relative effects used for the required comparison.
• The treatment pathway should represent relevant dosing, administration, monitoring and duration.
• Resource use and costs should reflect both the intervention and comparator pathways.
• Outcomes should be measured over the same population and time horizon.
• Perspective should be applied consistently to the costs and outcomes of every option.
• Subgroup analyses should use comparators relevant to each subgroup.
• Subsequent treatment and switching should be modelled consistently across alternatives.

A comparator can also change during an evaluation because of evolving practice, new technologies or price changes. The practice date and evidence cutoff used to define the comparator set should therefore be reported.

Testing uncertainty about the comparator

Comparator uncertainty is different from ordinary parameter uncertainty because it concerns which alternative represents the decision. Scenario analysis is especially important when several plausible comparators could produce different conclusions.

The evaluation should report comparator-specific results rather than averaging away meaningful differences. Alternative-comparator scenarios are particularly useful in the following circumstances.

• Practice varies materially between settings or patient groups.
• Several active alternatives are feasible.
• Treatment-mix weights are uncertain.
• Market shares or clinical pathways are changing.
• The principal comparator differs by subgroup.
• A near-market technology may alter practice.
• The clinical evidence uses a different control.
• The preferred option changes when the comparator changes.

Where comparator selection remains contested, the evaluation should explain the competing interpretations and show their consequences. Selecting a comparator because it produces a preferred result is not methodologically acceptable.

Implementing comparator analysis in Excel

An Excel model can use validated option selectors to retrieve the selected comparator’s costs and outcomes. The workbook should preserve visible option-level inputs and prevent self-comparison, missing selections and invalid treatment-mix weights.

The following formulas illustrate common implementation checks. Named ranges can be replaced with structured table references when appropriate.

Purpose	Example formula
Retrieve comparator cost	=XLOOKUP(SelectedComparator,OptionNames,TotalCosts)
Retrieve comparator outcome	=XLOOKUP(SelectedComparator,OptionNames,TotalOutcomes)
Calculate incremental cost	=InterventionCost-ComparatorCost
Calculate incremental outcome	=InterventionOutcome-ComparatorOutcome
Calculate an ICER with a safeguard	=IF(ABS(IncrementalOutcome)<EffectTolerance,NA(),IncrementalCost/IncrementalOutcome)
Calculate incremental net benefit	=Threshold*IncrementalOutcome-IncrementalCost
Calculate weighted comparator cost	=SUMPRODUCT(ComparatorWeights,ComparatorCosts)
Calculate weighted comparator outcome	=SUMPRODUCT(ComparatorWeights,ComparatorOutcomes)
Check that comparator weights sum to one	=IF(ABS(SUM(ComparatorWeights)-1)<Tolerance,"Pass","Check weights")
Check that a comparator was selected	=IF(COUNTIF(SelectedFlags,"Yes")=0,"Select comparator","Pass")
Prevent comparison with the same option	=IF(Intervention=SelectedComparator,"Invalid comparison","Pass")

A model containing several mutually exclusive options should also construct a fully incremental table. It should not rely solely on pairwise comparisons with a fixed baseline.

What should be reported about the comparator

Transparent reporting allows readers to judge whether the comparator represents the real decision problem. It also helps future analysts understand why results may differ across studies, jurisdictions or time periods.

A complete report should provide the following information.

• The evaluation should identify every comparator that was considered.
• The evaluation should name the base-case comparator and explain why it was selected.
• The evaluation should define current practice or standard care operationally.
• The evaluation should explain which treatments, services or resources the intervention is expected to displace.
• The evaluation should distinguish the clinical-evidence control from the economic comparator.
• The evaluation should describe adjustments used to align the evidence with the required comparison.
• The evaluation should report the components, weights and sources of any treatment-mix comparator.
• The evaluation should explain material subgroup or jurisdictional differences.
• The evaluation should state the practice date and evidence cutoff.
• The evaluation should identify relevant near-market alternatives.
• The evaluation should report comparator-specific incremental results.
• The evaluation should present uncertainty and alternative-comparator scenarios.

Common comparator errors

Comparator errors can make a technically sophisticated model answer the wrong decision question. They often arise when an available evidence source is allowed to determine the comparison without first defining the real alternatives.

The most important errors to avoid are listed below.

• Treating the clinical-trial control as automatically appropriate for the economic evaluation.
• Selecting an obsolete, unavailable or artificially weak comparator.
• Leaving usual care or standard care undefined.
• Describing no intervention while omitting continuing background care.
• Omitting a relevant active alternative.
• Comparing all options only with a fixed baseline when fully incremental analysis is required.
• Averaging alternatives that should be evaluated as separate policy choices.
• Using outdated treatment-mix weights.
• Ignoring material subgroup or jurisdictional variation.
• Confusing initial displacement with later treatment switching.
• Comparing an add-on intervention with no care.
• Misaligning the comparator used for effectiveness, costs and outcomes.
• Ignoring a near-market alternative that could change the decision.
• Selecting the comparator because it produces a preferred conclusion.