Objective To assess the association between industry sponsorship (drug, medical device, and biotechnology companies) and cost effectiveness results in cost effectiveness analysis (CEA). Design Registry based analysis Data source The Tufts Cost-Effectiveness Analysis Registry was used to identify all CEAs published in Medline between 1976 and 2021. Eligibility criteria for selecting studies CEAs that reported incremental cost effectiveness ratio (ICER) using quality adjusted life year and provided sufficient information about the magnitude or location of the ICER. Methods Descriptive analyses were used to describe and compare the characteristics of CEAs with and without industry sponsorship. Logistic regression was used to identify the association between industry sponsorship and the cost effective conclusion using selected threshold values ($50 000 (£40 511; €47 405), $100 000, and $150 000). Robust linear regression was used to assess the association between industry sponsorship and the magnitude of ICER. All regression analyses were adjusted for disease and study design characteristics. Results 8192 CEAs were eligible and included in the analysis, with 2437 (29.7%) sponsored by industry. Industry sponsored CEAs were more likely to publish ICERs below $50 000 (adjusted odds ratio 2.06, 95% confidence interval 1.82 to 2.33), $100 000 (2.95, 2.52 to 3.44), and $150 000 (3.34, 2.80 to 3.99) than non-industry sponsored studies. Among 5877 CEAs that reported positive incremental costs and quality adjusted life years, ICERs from industry sponsored studies were 33% lower (95% confidence interval −40 to −26) than those from non-industry sponsored studies. Conclusions Sponsorship bias in CEAs is significant, systemic, and present across a range of diseases and study designs. Use of CEAs conducted by independent bodies could provide payers with more ability to negotiate lower prices. This impartiality is especially important for countries that rely on published CEAs to inform policy making for insurance coverage because of limited capacity for independent economic analysis. No additional data available.

中文翻译：

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成本效益分析中的行业赞助偏差：基于注册的分析

目的 评估行业赞助（药品、医疗器械和生物技术公司）与成本效益分析 (CEA) 中的成本效益结果之间的关联。基于设计注册的分析 数据源 Tufts 成本效益分析注册用于识别 1976 年至 2021 年间在 Medline 上发表的所有 CEA。选择使用质量调整生命年报告增量成本效益比 (ICER) 并提供足够的 CEA 的研究的资格标准有关 ICER 的幅度或位置的信息。方法 描述性分析用于描述和比较有和没有行业赞助的 CEA 的特征。逻辑回归用于确定行业赞助与使用选定阈值（50 000 美元（40 511 英镑；47 405 欧元）、100 000 美元和 150 000 美元）的成本效益结论之间的关联。稳健的线性回归用于评估行业赞助与 ICER 大小之间的关联。所有回归分析都针对疾病和研究设计特征进行了调整。结果 8192 个 CEA 符合条件并被纳入分析，其中 2437 个（29.7%）由行业赞助。与非行业赞助的 CEA 相比，行业赞助的 CEA 更有可能发布低于 5 万美元（调整优势比 2.06，95% 置信区间 1.82 至 2.33）、10 万美元（2.95、2.52 至 3.44）和 15 万美元（3.34、2.80 至 3.99）的 ICER。行业赞助的研究。在报告增加成本和质量调整生命年为正的 5877 个 CEA 中，来自行业赞助研究的 ICER 比来自非行业赞助研究的 ICER 低 33%（95% 置信区间 -40 到 -26）。结论 CEA 中的赞助偏倚是显着的、系统性的，并且存在于一系列疾病和研究设计中。使用由独立机构进行的 CEA 可以为付款人提供更多谈判更低价格的能力。由于独立经济分析能力有限，这种公正性对于依赖已公布的 CEA 为保险政策制定提供信息的国家尤为重要。没有可用的额外数据。使用由独立机构进行的 CEA 可以为付款人提供更多谈判更低价格的能力。由于独立经济分析能力有限，这种公正性对于依赖已公布的 CEA 为保险政策制定提供信息的国家尤为重要。没有可用的额外数据。使用由独立机构进行的 CEA 可以为付款人提供更多谈判更低价格的能力。由于独立经济分析能力有限，这种公正性对于依赖已公布的 CEA 为保险政策制定提供信息的国家尤为重要。没有可用的额外数据。