Sponsorship Bias in Base-Case Values and Uncertainty Bounds of Health Economic Evaluations? A Systematic Review of Herpes Zoster Vaccination

Abstract

Background. New health technologies are more likely adopted when they have lower incremental cost-effectiveness ratios (ICERs) and/or when their ICER is presented with more certainty. Industry-funded (IF) health economic evaluations use often more favorable base-case values, leading to more favorable conclusions. Purpose. To study whether IF health economic evaluations of varicella-zoster virus vaccination in the elderly use more favorable base-case values and account for less uncertainty than non–industry-funded (NIF) evaluations. Methods. Data source: PubMed. Data extracted: funding source; incremental cost per quality-adjusted life year (QALY) gained; vaccine price; study quality score; base-case values, uncertainty ranges, and data sources for influential parameters: duration of vaccine protection, utility loss due to herpes zoster (HZ) disease, percentage of HZ patients developing postherpetic neuralgia (PHN), and duration of PHN. Data synthesis: qualitative comparisons; Fisher exact test for differences in study quality score and 1-sided Mann-Whitney U tests for differences in base-case values and uncertainty ranges. Results. Despite using the same data sources, IF studies (n = 10) assume a longer duration of vaccine protection (U = 56, P = 0.03), have a higher percentage of HZ patients developing PHN (U = 22/33, P = 0.02/0.03 for ages 60–64/65–69), and tend to use higher HZ utility loss than NIF studies (n = 11) for their baseline. IF studies show lower ICERs given similar or even higher vaccine prices than NIF studies, consider less uncertainty around the duration of vaccine protection (U = 8, P < 0.001), and tend to use less uncertainty around the duration of PHN. Yet their quality has been rated equally well, using current standard quality rating tools. Conclusion. Researchers and decision makers should be aware of potential sponsorship bias in health economic evaluations, especially in the way source data are used to specify base-case values and uncertainty ranges.