There are increasing numbers of estimates of opportunity cost to inform the setting of thresholds as ceiling cost-per-quality-adjusted life year (QALY) ratios. To understand their ability to inform policy making, we need to understand the degree of uncertainty surrounding these estimates. In particular, do estimates provide sufficient certainty that the current policy “rules” or “benchmarks” need revision? Does the degree of uncertainty around those estimates mean that further evidence generation is required?
We analyse uncertainty and methods from three papers that focus on the use of data from the NHS in England to estimate opportunity cost. All estimate the impact of expenditure on mortality in cross-sectional regression analyses and then translate the mortality elasticities into cost-per-QALY thresholds using the same assumptions. All three discuss structural uncertainty around the regression analysis, and report parameter uncertainty derived from their estimated standard errors. However, only the initial, seminal, paper explores the structural uncertainty involved in moving from the regression analysis to a threshold. We discuss the elements of structural uncertainty arising from the assumptions that underpin the translation of elasticities to thresholds and seek to quantify the importance of some of the effects.
We find several sets of plausible structural assumptions that would place the threshold estimates from these studies within the current National Institute for Health and Care Excellence (NICE) range of £20,000 to £30,000 per QALY. Heterogeneity, an additional source of uncertainty from variability, is also discussed and reported.
Lastly, we discuss how decision uncertainty around the threshold could be reduced, setting out what sort of additional research is required, notably in improving estimates of disease burden and of the impact of health expenditure on quality of life. Given the likely value to policy makers of this research it should be a priority for health system research funding.