Objectives: The aim of this study was to demonstrate how value of information analysis can measure the upper limit on returns to future research and identify the research priorities for computer-assisted total knee replacement (CAS-TKR).
Methods: Using a previous economic analysis of CAS-TKR compared with conventional TKR, the population expected value of perfect information (EVPI) was calculated using Monte Carlo simulation to provide an estimate of the upper limit on returns to future research. The population expected value of partial perfect information (EVPPI) for both individual parameters and groups of parameters was estimated to inform specific future research priorities.
Results: The UK individual EVPI would be £21.4 if the willingness to pay for one QALY (quality-adjusted life-year) were £30,000. The population EVPPI would be £8.3 million, assuming a 10-year time horizon for CAS-TKR. In this instance, the expected value of information is positively related to willingness to pay for one QALY for the range of £0 to £50,000. Although each individual parameter had an EVPPI of £0, groups of utility parameters had positive EVPPI. Population EVPPI was £5.6 million for utility parameters, £20,000 for transition probabilities relating to CAS-TKR, and £5,000 for transition probabilities related to conventional TKR.
Conclusions: The study provides evidence on which parameters further information may be of most value. Focusing research on the utility values associated with health states relating to TKR would be of greatest value.