Alejandra Castanon, Antonia Tsvetanova, Sreeram V Ramagopalan
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引用次数: 0
摘要
在本次更新中,我们讨论了美国 FDA 最近的指导意见,该指导意见就适当的研究设计和分析提供了更具体的指导,以支持非干预性研究的因果推断,以及欧洲药品管理局 (EMA) 和药品管理局主管机构 (HMA) 公共电子目录的推出。我们还重点介绍了一篇建议在最终确定方案之前评估数据质量和适宜性的文章,以及《美国医学会杂志》认可的在发布真实世界证据研究时使用因果语言的框架。最后,我们探讨了大型语言模型在自动开发健康经济模型方面的潜力。
RWE ready for reimbursement? A round up of developments in real-world evidence relating to health technology assessment: part 16.
In this update, we discuss recent US FDA guidance offering more specific guidelines on appropriate study design and analysis to support causal inference for non-interventional studies and the launch of the European Medicines Agency (EMA) and the Heads of Medicines Agencies (HMA) public electronic catalogues. We also highlight an article recommending assessing data quality and suitability prior to protocol finalization and a Journal of the American Medical Association-endorsed framework for using causal language when publishing real-world evidence studies. Finally, we explore the potential of large language models to automate the development of health economic models.
期刊介绍:
Journal of Comparative Effectiveness Research provides a rapid-publication platform for debate, and for the presentation of new findings and research methodologies.
Through rigorous evaluation and comprehensive coverage, the Journal of Comparative Effectiveness Research provides stakeholders (including patients, clinicians, healthcare purchasers, and health policy makers) with the key data and opinions to make informed and specific decisions on clinical practice.