Decision Rule

Decision rules make the analytical logic behind a recommendation explicit. The sections below explain how comparative cost and outcome evidence is translated into a conditional recommendation using dominance, incremental cost-effectiveness ratios and net benefit. They also explain how uncertainty, budget impact and the wider health technology assessment process affect the final decision.

A decision rule does not remove judgement or guarantee adoption. The rule must match the decision context, use the relevant threshold or constraint, and distinguish a cost-effectiveness conclusion from a complete reimbursement or coverage decision.

What a decision rule does

A decision rule states how evidence will be interpreted before the result is known. In economic evaluation, the rule commonly compares additional costs with additional health outcomes and applies a stated cost-effectiveness threshold.

The output is a conditional recommendation, not an automatic command. Decision-makers may also need to consider uncertainty, affordability, equity, severity, implementation and other responsibilities within their remit.

• A decision rule identifies the alternatives being compared and the outcome that determines preference.
• A cost-effectiveness decision rule states the threshold or other value used to judge whether additional health is worth the additional cost.
• A decision rule should specify how dominance, uncertainty and ties are handled.
• A decision rule should distinguish the economic-evaluation result from the final institutional recommendation.

Start with the decision question

The rule can only be interpreted correctly when the population, alternatives, perspective, outcomes and time horizon are clear. These choices determine which costs and health effects enter the comparison and whose opportunity costs matter.

The comparator is especially important because cost-effectiveness is incremental. A technology may appear attractive against one option but not against the next-best relevant alternative.

• The decision population identifies the people to whom the recommendation would apply.
• The alternatives identify the healthcare options that must be compared.
• The analytical perspective determines which costs and consequences enter the economic evaluation.
• The time horizon should capture all material differences between the alternatives.
• The outcome measure and cost-effectiveness threshold establish how benefits and opportunity costs are valued.

How the rule works for two options

For two options, the decision rule can be expressed with incremental net monetary benefit or incremental net health benefit. Both forms compare the gain in health with the opportunity cost represented by the additional expenditure.

Let ΔE be the incremental health effect, ΔC the incremental cost and λ the cost-effectiveness threshold. A positive result favours the evaluated option on cost-effectiveness grounds, zero represents a tie at the stated threshold, and a negative result favours the comparator.

INMB = (λ × ΔE) − ΔC

INHB = ΔE − (ΔC ÷ λ)

• A positive incremental net monetary benefit means the valued incremental health gain exceeds the incremental cost at the stated threshold.
• A zero incremental net monetary benefit means the alternatives are tied under the stated cost-effectiveness rule.
• A negative incremental net monetary benefit means the evaluated option is not preferred on cost-effectiveness grounds at the stated threshold.
• The threshold must be reported with its currency, price year, health unit, jurisdiction and decision context.

Why an ICER cannot be interpreted by its sign alone

The incremental cost-effectiveness ratio divides incremental cost by incremental effect, but the same sign can arise in different quadrants of the cost-effectiveness plane. A negative ICER may describe either a dominant option or a dominated option, so the directions of cost and effect must be checked before the ratio is interpreted.

Dominance provides the decision before a threshold comparison is needed. The four possible combinations are:

• More effective and less costly: The evaluated option strictly dominates the comparator.
• More effective and more costly: Compare the ICER or net benefit with the threshold.
• Less effective and less costly: Compare the savings with the health forgone using net benefit or a threshold rule.
• Less effective and more costly: The evaluated option is strictly dominated by the comparator.

How to compare several alternatives

Several alternatives should be compared together rather than through an arbitrary collection of pairwise ratios. Fully incremental analysis removes inefficient options and identifies the cost-effectiveness frontier before the remaining alternatives are judged against the threshold.

The order of analysis matters because each non-dominated option is compared with the next-less-costly option on the frontier. Expected net benefit offers an equivalent way to identify the preferred option when the same threshold, perspective and evidence apply to all alternatives.

1. Order the alternatives from lowest to highest expected cost.
2. Remove any alternative that is strictly dominated by an option that costs less and produces more health.
3. Calculate sequential incremental cost-effectiveness ratios for the remaining alternatives.
4. Remove any alternative subject to extended dominance because a combination of other options produces health more efficiently.
5. Recalculate the frontier after each removal.
6. Identify the alternative with the highest expected net benefit at the stated threshold.

A worked two-option example

Suppose a new programme produces 0.4 additional quality-adjusted life-years per person and costs an additional £8,000 compared with current care. At a threshold of £30,000 per quality-adjusted life-year, the incremental net monetary benefit is £4,000.

The positive incremental net monetary benefit means the programme is cost-effective under the stated assumptions and threshold. It does not by itself establish that the programme is affordable or that an HTA body should recommend unrestricted adoption.

INMB = (£30,000 × 0.4) − £8,000 = £4,000

• The worked example uses synthetic values and does not represent a real technology or institutional decision.
• The worked example's conclusion changes if the incremental cost, incremental effect or threshold changes enough to make incremental net monetary benefit zero or negative.
• The worked example requires separate budget-impact evidence to assess the total financial consequences of adoption.

How uncertainty changes the interpretation

Expected net benefit is the appropriate basis for choosing the option that maximises expected value under the stated model and threshold. The probability that an option is cost-effective answers a different question: how often that option has the highest net benefit across uncertainty draws.

An option can have the highest expected net benefit without having the highest probability of being cost-effective. Decision uncertainty may justify further research when additional evidence could change the preferred choice and the value of resolving uncertainty exceeds the cost of research.

• Expected net benefit identifies the option with the greatest average net benefit across the modelled uncertainty.
• The probability of cost-effectiveness describes decision uncertainty and is not itself the decision rule.
• A cost-effectiveness acceptability curve should not replace expected net benefit when selecting the preferred alternative.
• Value-of-information analysis can assess whether reducing uncertainty may be worthwhile.

How the decision rule relates to budget impact and HTA

Cost-effectiveness analysis and budget impact analysis answer related but different questions. The cost-effectiveness decision rule asks whether the additional health is worth the opportunity cost, whereas budget impact analysis estimates the change in spending for a defined budget holder over a stated period.

Health technology assessment may use both results alongside clinical evidence, equity, severity, feasibility and other considerations. A positive incremental net benefit can therefore support a recommendation without guaranteeing adoption, unrestricted access or immediate implementation.

• Economic evaluation informs the decision rule by providing comparative estimates of costs and consequences.
• Budget impact analysis informs the wider decision by estimating affordability for a defined budget holder.
• Health technology assessment uses the decision rule within a broader appraisal process rather than treating it as the complete decision.
• Equity, severity or implementation considerations should be stated explicitly when they modify the final recommendation.

Common mistakes and how to avoid them

Decision-rule errors often come from interpreting a ratio without its quadrant, comparing the wrong alternatives or treating one institution's threshold as universal. These mistakes can reverse the apparent conclusion or overstate what the economic evidence establishes.

The safeguard is to state the decision context and apply the analytical steps in a reproducible order. When judgement changes the recommendation, the reason should be visible rather than hidden inside the calculation.

• Interpreting every negative ICER as favourable can confuse dominance with being dominated.
• Comparing every option only with a common baseline can miss strict or extended dominance in a multi-option analysis.
• Selecting the option with the highest probability of cost-effectiveness can differ from selecting the option with the highest expected net benefit.
• Changing the threshold after seeing the result can introduce outcome-driven reasoning unless the change is a declared scenario analysis.
• Calling an option cost-effective without naming the comparator, perspective, threshold and uncertainty can make the statement impossible to interpret.
• Treating cost-effectiveness as affordability can overlook the total spending consequences measured by budget impact analysis.

What should be reported

A transparent report should let a reader reconstruct the decision rule and understand the limits of its conclusion. The report should separate model outputs from the institutional or deliberative judgement that follows them.

The following information makes the result auditable by people and reusable by search and AI systems. Jurisdiction-specific values should include the guidance version and date that governed the analysis.

• Report the population, alternatives, perspective, time horizon and outcome measure used in the economic evaluation.
• Report incremental costs and incremental effects for every relevant comparison.
• Report dominance and extended-dominance checks for analyses with several alternatives.
• Report the threshold, currency, price year, health unit, jurisdiction and source.
• Report incremental net monetary benefit or incremental net health benefit and explain its sign.
• Report expected net benefit and decision uncertainty for all relevant alternatives.
• Report the budget impact separately from the cost-effectiveness conclusion.
• Report any equity, severity, implementation or institutional judgement that changes the recommendation.
• Report scenario analyses without replacing the prespecified base-case decision rule.