The Estimand Framework and Causal Inference: Complementary not Competing Paradigms
目标估计量框架与因果推断：相互补充的范式AI 解读 ↓
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目标估计量框架与因果推断：相互补充的范式
- 研究问题
- 临床试验中的ICH E9（R1）目标估计量框架与因果推断如何关联，两者怎样共同明确目标人群、结局和效应，并改善设计与结果沟通？
- 方法与思路
- 作者通过示例比较两种框架对人群层面效应的定义，区分描述性结构化表达与数学化因果定义，并讨论用因果图显式陈述假设。
- 材料陈述的贡献
- 摘要称目标估计量框架便于非数学背景人员沟通，而因果框架使定义和假设更精确；结合使用有助于明确临床试验究竟估计什么。
- 阅读关注点
- 应核对示例中目标人群和效应定义如何对应，两类框架分别解决哪些沟通与识别问题，以及明确目标后仍需哪些估计假设。
- 阅读边界
- 这是arXiv预印本，不能据此认定已同行评审或期刊接收。摘要主要提供概念比较，未给出新估计量或数值证据，沟通清晰不等于效应已识别。
原文摘要
The creation of the ICH E9 (R1) estimands framework has led to more precise specification of the treatment effects of interest in the design and statistical analysis of clinical trials. However, it is unclear how the new framework relates to causal inference, as both approaches appear to define what is being estimated and have a quantity labelled an estimand. Using illustrative examples, we show that both approaches can be used to define a population-based summary of an effect on an outcome for a specified population and highlight the similarities and differences between these approaches. We demonstrate that the ICH E9 (R1) estimand framework offers a descriptive, structured approach that is more accessible to non-mathematicians, facilitating clearer communication of trial objectives and results. We then contrast this with the causal inference framework, which provides a mathematically precise definition of an estimand, and allows the explicit articulation of assumptions through tools such as causal graphs. Despite these differences, the two paradigms should be viewed as complementary rather than competing. The combined use of both approaches enhances the ability to communicate what is being estimated. We encourage those familiar with one framework to appreciate the concepts of the other to strengthen the robustness and clarity of clinical trial design, analysis, and interpretation.