The role of value-of-information analysis in a health care research priority setting: a theoretical case study.

Background: The Dutch reimbursement procedure for expensive drugs requires the submission of a baseline cost-effectiveness (CE) analysis and a research plan for the period of temporary reimbursement to estimate the real-life cost-effectiveness after 4 years. The Dutch guidelines recommend a value-of-information analysis to identify the critical parameters to be studied in such an outcome study.

Objectives: Identify situations where sensitivity analyses are sufficient to establish the need for additional data collection and priority setting.

Methods: We used a hypothetical Markov model with 3 groups of parameters. We performed deterministic and probabilistic sensitivity analyses (PSA) and analyzed the expected value of partial perfect information (EVPPI), for different configurations of input parameters and a range of threshold incremental cost-effectiveness ratios (λ). We introduced a multivariate (deterministic) sensitivity analysis and a partial PSA.

Results: Deterministic, partial PSA, and EVPPI analyses came to the same ranking of priorities for future research in most cases, irrespective of the place of the results on the CE plane. Rankings differed only when the statistical metrics that we calculated for each method were close together.

Conclusions: When a clear ranking can be established, all methods lead to the same priority setting. If there is no clear ranking, we regard the parameters as equally important. Priority setting for future research depends on λ and the location of results on the CE plane. The EVPPI is needed to estimate the value of doing additional research, but to prioritize parameters for further research, extensive (partial probabilistic) sensitivity analyses and expected value of perfect information are often sufficient.