本文由小咖机器人翻译整理

期刊来源：JAMA

原文链接：https://doi.org/10.1001/jama.2024.7741

摘要内容如下：

重要性

包括《美国医学会杂志》在内的许多医学期刊都将因果语言的使用限制在随机临床试验的报告中。尽管进行良好的随机临床试验仍然是回答因果问题的首选方法，但观察性研究的方法已经取得了进步，当强有力的假设成立时，对进行良好的观察性研究的结果进行因果解释是可能的。此外，观察性研究可能是回答有关医疗或政策干预的因果效应的一些问题的唯一实用信息来源，可以支持对反映实践的人群和环境中的干预措施的研究，并可以帮助确定进一步实验研究的干预措施。在描述观察性研究时，确定适当使用因果语言的机会对于医学期刊的交流非常重要。

观察

在描述观察性研究时，是否以及如何使用因果语言的结构化方法将加强研究目标的沟通，支持对假设、设计和分析选择的评估，并允许对结果进行更清晰和准确的解释。基于跨越不同学科的因果推理的大量文献，我们提出了一个观察性研究的框架，旨在基于6个核心问题提供关于干预的因果效应的证据：什么是因果问题；如果知道的话，什么数量可以回答这个因果问题？研究设计是什么？作出了哪些因果假设；如何使用观测数据来回答原则上和实践中的因果问题？对这些分析的因果解释是否站得住脚？

结论和相关性

在观察性研究中，采用提议的框架来确定何时因果解释是适当的，这有助于促进作者、审稿人、编辑和读者之间更好的沟通。实际实施将需要编辑、作者和审稿人之间的合作，以实施该框架并评估其对实证研究报告的影响。

英文原文如下：

Abstracts

Importance  Many medical journals, including JAMA, restrict the use of causal language to the reporting of randomized clinical trials. Although well-conducted randomized clinical trials remain the preferred approach for answering causal questions, methods for observational studies have advanced such that causal interpretations of the results of well-conducted observational studies may be possible when strong assumptions hold. Furthermore, observational studies may be the only practical source of information for answering some questions about the causal effects of medical or policy interventions, can support the study of interventions in populations and settings that reflect practice, and can help identify interventions for further experimental investigation. Identifying opportunities for the appropriate use of causal language when describing observational studies is important for communication in medical journals.

Observations  A structured approach to whether and how causal language may be used when describing observational studies would enhance the communication of research goals, support the assessment of assumptions and design and analytic choices, and allow for more clear and accurate interpretation of results. Building on the extensive literature on causal inference across diverse disciplines, we suggest a framework for observational studies that aim to provide evidence about the causal effects of interventions based on 6 core questions: what is the causal question; what quantity would, if known, answer the causal question; what is the study design; what causal assumptions are being made; how can the observed data be used to answer the causal question in principle and in practice; and is a causal interpretation of the analyses tenable?

Conclusions and Relevance  Adoption of the proposed framework to identify when causal interpretation is appropriate in observational studies promises to facilitate better communication between authors, reviewers, editors, and readers. Practical implementation will require cooperation between editors, authors, and reviewers to operationalize the framework and evaluate its effect on the reporting of empirical research.

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