Utility Value Sets and Health-State Decrements

Definition and scope

Selection and application of a preference-based valuation algorithm and justified utility differences for health states, events, or treatment effects in an economic evaluation. The target population, use, and analysis remain protocol decisions; this card explains the choices and their consequences rather than approving a study.

Operational specification

Record the instrument/version, health-state profile, country or jurisdiction, valuation population, algorithm/version, valid score range, and intended use. Keep the descriptive health profile separate from the preference weights assigned to it. Distinguish a directly measured state utility, an event decrement, and a mapped utility prediction.

Analysis and diagnostics

Use the selected value set consistently, preserve uncertainty and dependencies, and justify how concurrent events or comorbidities combine. If applying an additive decrement, specify duration and avoid double counting effects already captured by the state utility. Compare plausible alternative valuation or combination assumptions when they could alter the decision.

When NOT to use and common errors

Do not apply a value set to the wrong instrument version, assume another country's tariff is interchangeable, truncate all negative utilities, or add unrelated decrements indefinitely without checking the resulting interpretation and feasible range. A utility decrement is not automatically a causal treatment effect.

Worked example and implementation boundary

A synthetic model uses baseline utility 0.8 and an illustrative additive event decrement of 0.1 lasting one quarter-year. Compute the QALY loss for that interval only. The illustrative QALY loss is 0.025. The decrement and duration require independent justification in an actual model. The code performs that explicitly stated calculation with synthetic inputs. It is not a complete fitted analysis and does not demonstrate the identification assumptions, estimate sampling uncertainty, or certify application in another population.

Data-source and reporting requirements

Preserve the provenance, dates, units, and observation rule for each input. Claims capture services and transactions under observable benefits; absence of a record is not proof that an event or resource use did not occur. EHR and registry measurements depend on the collection process and may miss care outside the system. Linked sources add reconciliation and selection questions. Primary collection can improve a missing domain but still requires valid measurement and follow-up. Report which of these limitations affects the stated use, which evidence supports the chosen inputs, and what remains uncertain.

Worked example

Scenario

A synthetic model uses baseline utility 0.8 and an illustrative additive event decrement of 0.1 lasting one quarter-year. Compute the QALY loss for that interval only.

Dataset

Synthetic inputs with units defined in the scenario; no patient data.

Steps

Result

The illustrative QALY loss is 0.025. The decrement and duration require independent justification in an actual model.

Trade-offs

Code example

decrement = 0.1
duration_years = 0.25
result = decrement*duration_years
assert abs(result - 0.025) < 1e-9
print(result)

decrement <- 0.1
duration_years <- 0.25
result <- decrement*duration_years
stopifnot(abs(result - 0.025) < 1e-9)
print(result)

Citations

1. [1]Brooks R. EuroQol: the current state of play. Health Policy. 1996.
2. [2]Pickard AS, Law EH, Jiang R, Pullenayegum E, Shaw JW, Xie F, Oppe M, Boye KS, Chapman RH, Gong CL, Balch AH, Busschbach JJV. United States Valuation of EQ-5D-5L Health States Using an International Protocol. Value in Health. 2019;22(8):931-941.
3. [3]Husereau D, et al. Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 Explanation and Elaboration: A Report of the ISPOR CHEERS II Good Practices Task Force. Value in Health. 2022.