Longitudinal cohort studies, which follow a group of individuals over time, provide the opportunity to examine causal effects of complex exposures on long-term health outcomes. Utilizing data from multiple cohorts has the potential to add further benefit by improving precision of estimates through data pooling and allowing examination of effect heterogeneity. However, the interpretation of findings can be complicated by biases that may: i) be compounded when pooling data; or, ii) contribute to discrepant findings when analyses are replicated across cohorts. The 'target trial' is a well-established and powerful tool for guiding causal inference in single-cohort studies. Here we extend this conceptual framework to address the specific challenges that can arise in the multi-cohort setting. By representing a clear definition of the target estimand, the target trial provides a central point of reference against which bias arising in each study and from data pooling can be systematically assessed. Consequently, analyses can be designed to reduce these biases and the resulting findings appropriately interpreted in light of potential remaining biases. We use a case study to demonstrate the framework and its potential to strengthen causal inference in multi-cohort studies through improved analysis design and clarity in the interpretation of findings.

翻译：纵向队列研究通过长期追踪一组个体，为评估复杂暴露因素对长期健康结局的因果效应提供了研究机会。利用多个队列的数据通过数据合并提高估计精度，并允许检验效应异质性，可进一步增加研究价值。然而，研究结果的解读可能因以下偏倚而变得复杂：（i）数据合并时偏倚可能被叠加放大；或（ii）当分析在不同队列中重复时，偏倚可能导致结果不一致。'目标试验'是指导单队列研究因果推断的成熟且有力的工具。本文将该概念框架扩展至多队列场景，以应对该情境下出现的特有挑战。通过明确定义目标估计量，目标试验为系统评估各研究及数据合并中产生的偏倚提供了核心参考基准。据此，可设计分析方案以减少这些偏倚，并基于潜在残余偏倚对研究结果进行恰当解读。我们通过案例研究展示了该框架如何通过改进分析设计和提升结果解读清晰度，强化多队列研究中的因果推断能力。