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 methodsRosenbaum’s approachRosenbaum-RubinBayesian hierarchical/twin-regression modellingSimulation basedDerived bias formulasEvidence 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.

本数据集为发表于《比较有效性研究杂志》的论文《德国心血管单药联合治疗方案与相同多药治疗方案在医疗保健成本和利用率方面的影响》的同行评审补充材料。QBA方法概要：罗森鲍姆方法、罗森鲍姆-鲁宾方法、贝叶斯层次/双生子回归建模、基于模拟的方法、推导偏差公式。来自非随机研究（NRS）的证据日益提交给健康技术评估（HTA）机构。未测量的混杂因素是该类型证据的主要关注点，因为它可能导致治疗效果估计的偏差，这导致了HTA机构对NRS的广泛批评。定量偏差分析是一组在流行病学文献中开发出来的方法，旨在量化未测量混杂因素的影响并调整来自NRS的效果估计。本文提出的适用于HTA的关键考虑因素反映了需要在方法学的复杂性与应用的简便性和可解释性之间取得平衡，以及确保方法符合HTA机构评估非随机证据的现有框架的需求。