Supplementary materials: Unmeasured confounding in nonrandomized studies: quantitative bias analysis in health technology assessment

These are peer-reviewed supplementary materials for the article 'Effects of cardiovascular single pill combinations compared with identical multi-pill therapies on healthcare cost and utilization in Germany' published in the Journal of Comparative Effectiveness Research.

Summary of QBA methods

• Rosenbaum’s approach
• Rosenbaum-Rubin
• Bayesian hierarchical/twin-regression modelling
• Simulation based
• Derived bias formulas

Evidence generated from nonrandomized studies (NRS) is increasingly submitted to health technology assessment (HTA) agencies. Unmeasured confounding is a primary concern with this type of evidence, as it may result in biased treatment effect estimates, which has led to much criticism of NRS by HTA agencies. Quantitative bias analyses are a group of methods that have been developed in the epidemiological literature to quantify the impact of unmeasured confounding and adjust effect estimates from NRS. Key considerations for application in HTA proposed in this article reflect the need to balance methodological complexity with ease of application and interpretation, and the need to ensure the methods fit within the existing frameworks used to assess nonrandomized evidence by HTA bodies.