The ideal trial: defining causal estimands that balance relevance and feasibility in target trial emulations and actual randomized trials
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理想试验：在目标试验模拟与随机试验中平衡因果目标的相关性和可行性
- 研究问题
- 目标试验和实际随机试验应如何定义因果估计目标，才能兼顾现实决策相关性与可估计性，并识别剩余偏差？
- 方法与思路
- 作者回顾理想试验概念，比较理想试验、目标试验及其模拟与实际试验，并以呼吸流行病学例子说明差异。
- 材料陈述的贡献
- 摘要主张，过度按观测数据对齐目标试验可能偏离最终关心的因果目标，应通过理想试验系统识别和管理偏差来源。
- 阅读关注点
- 应核对理想试验如何界定人群与干预、数据对齐改变了哪些估计目标，以及不同偏差来源如何区分和处理。
- 阅读边界
- 这是预印本，同行评审或期刊接收未核实；摘要主要提供概念论证，未说明统一估计方法或偏差修正的形式保证。
原文摘要
Causal inference is the goal of randomized trials and many observational studies. The first step in a formal causal inference framework is to define the causal estimand, and in both types of study this can be intuitively defined as the effect in an ideal trial: a hypothetical perfect randomized experiment (with representative sample, perfect adherence, etc.). The target trial framework is increasingly used for causal inference in observational studies, but clarity is lacking in how a target trial should be specified and how it relates to an ideal trial. In this paper, we review the concept of the ideal trial and highlight the need to balance relevance for decision-making in the real world and feasibility of estimation when specifying it. We then consider the question of how a target trial should be specified, outlining the challenges of a recommended approach, commonly seen in applications, that puts the focus heavily on feasibility of estimation: to specify the target trial such that it is closely aligned with the observational data (e.g. uses the same eligibility criteria). We argue that with this "aligned" approach, biases may remain relative to the estimand of ultimate practical interest, defined by the ideal trial, which mirror the often-overlooked biases of actual trials. We conclude that consideration of the ideal trial and of how the target trial and its emulation or the actual trial differ from it is necessary to identify and manage all bias sources in both settings. An example from respiratory epidemiology is used for illustration.