QALY Accrual from Repeated Utility Measurements

Definition and scope

Integration of health-state utility over time to estimate quality-adjusted life-years, using explicit measurement timing, interpolation, survival, and missingness assumptions. The target population, use, and analysis remain protocol decisions; this card explains the choices and their consequences rather than approving a study.

Operational specification

Specify the preference-based instrument and value set, time origin, utility dates, time units, interpolation between measurements, death date, and follow-up horizon. Distinguish a missing utility for an alive person from a post-death period assigned no living health time. Utility can be negative for states valued worse than dead; do not clip to zero unless the target explicitly requires a transformed outcome.

Analysis and diagnostics

For complete repeated observations, calculate area under the utility-time curve under a justified interpolation rule. In comparisons, account for baseline utility and design/causal adjustment; assess informative missingness and differences in measurement frequency. Report extrapolation after the last measurement separately from observed utility accrual and propagate uncertainty.

When NOT to use and common errors

Do not average utility scores without accounting for unequal intervals, multiply a non-preference-based symptom score directly by years, impute arbitrary terminal utilities, or assume an area-under-the-curve calculation removes confounding. Mapping to utility adds its own prediction uncertainty.

Worked example and implementation boundary

A synthetic patient is observed alive for one year with utilities 0.8 at baseline, 0.6 at six months, and 0.4 at one year. Use linear interpolation for this teaching calculation. The one-year area is 0.60 QALYs under the stated interpolation rule. It is not a treatment-effect estimate. 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 patient is observed alive for one year with utilities 0.8 at baseline, 0.6 at six months, and 0.4 at one year. Use linear interpolation for this teaching calculation.

Dataset

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

Steps

Result

The one-year area is 0.60 QALYs under the stated interpolation rule. It is not a treatment-effect estimate.

Trade-offs

Code example

u0 = 0.8
u1 = 0.6
u2 = 0.4
interval_years = 0.5
result = ((u0+u1)/2+(u1+u2)/2)*interval_years
assert abs(result - 0.6) < 1e-9
print(result)

u0 <- 0.8
u1 <- 0.6
u2 <- 0.4
interval_years <- 0.5
result <- ((u0+u1)/2+(u1+u2)/2)*interval_years
stopifnot(abs(result - 0.6) < 1e-9)
print(result)

Citations

1. [1]Manca A, Hawkins N, Sculpher MJ. Estimating mean QALYs in trial-based cost-effectiveness analysis: the importance of controlling for baseline utility. Health Economics. 2005;14(5):487-496.
2. [2]Brooks R. EuroQol: the current state of play. Health Policy. 1996.