The target trial framework enables causal inference from longitudinal observational data by emulating randomized trials initiated at multiple time points. Precision is often improved by pooling information across trials, with standard models typically assuming - among other things - a time-constant treatment effect. However, this obscures interpretation when the true treatment effect varies, which we argue to be likely as a result of relying on noncollapsible estimands. To address these challenges, this paper introduces a model-free strategy for target trial analysis, centered around the choice of the estimand, rather than model specification. This ensures that treatment effects remain clearly interpretable for well-defined populations even under model misspecification. We propose estimands suitable for different study designs, and develop accompanying G-computation and inverse probability weighted estimators. Applications on simulations and real data on antimicrobial de-escalation in an intensive care unit setting demonstrate the greater clarity and reliability of the proposed methodology over traditional techniques.

翻译：目标试验框架通过模拟在多个时间点启动的随机试验，实现了从纵向观测数据进行因果推断。通过跨试验合并信息通常能提高精度，标准模型通常假设——除其他因素外——存在时间恒定的处理效应。然而，当真实处理效应发生变化时，这种假设会模糊解释，我们认为这种变化很可能是由于依赖非可压缩估计量所致。为应对这些挑战，本文引入了一种用于目标试验分析的无模型策略，其核心在于估计量的选择，而非模型设定。这确保了即使在模型设定错误的情况下，处理效应对于明确定义的总体仍能保持清晰的可解释性。我们提出了适用于不同研究设计的估计量，并开发了相应的G-计算和逆概率加权估计器。在模拟数据及重症监护室环境下抗菌药物降阶梯治疗的真实数据上的应用表明，与传统技术相比，所提方法具有更高的清晰度和可靠性。