Background: Regulatory authorities are approving innovative therapies with limited evidence. Although this level of data is sufficient for the regulator to establish an acceptable risk-benefit balance, it is problematic for downstream health technology assessment, where assessment of cost-effectiveness requires reliable estimates of effectiveness relative to existing clinical practice. Some key issues associated with a limited evidence base include using data, from nonrandomized studies, from small single-arm trials, or from single-center trials; and using surrogate end points.
Methods: We examined these methodological challenges through a pragmatic review of the available literature.
Results: Methods to adjust nonrandomized studies for confounding are imperfect. The relative treatment effect generated from single-arm trials is uncertain and may be optimistic. Single-center trial results may not be generalizable. Surrogate end points, on average, overestimate treatment effects. Current methods for analyzing such data are limited, and effectiveness claims based on these suboptimal forms of evidence are likely to be subject to significant uncertainty.
Conclusion: Assessments of cost-effectiveness, based on the modeling of such data, are likely to be subject to considerable uncertainty. This uncertainty must not be underestimated by decision makers: methods for its quantification are required and schemes to protect payers from the cost of uncertainty should be implemented.
Keywords: Accelerated approval; Cost–effectiveness; Nonrandomized studies; Single-arm trials; Single-center trials; Surrogate outcomes.
Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.