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 by allowing examination of effect heterogeneity through replication of analyses across cohorts. However, the interpretation of findings can be complicated by biases that may be compounded when pooling data, or, contribute to discrepant findings when analyses are replicated. The "target trial" is a 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 biases arising in each cohort 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特定人群，为探究复杂暴露因素对长期健康结局的因果效应提供了可能。利用多队列数据能进一步带来双重优势：一方面通过数据整合提高估计精度，另一方面通过跨队列分析复现实现对效应异质性的检验。然而，研究结果的解释可能因偏倚问题而复杂化——这些偏倚在数据整合时可能被放大，或在分析复现时导致结论不一致。"目标试验"是指导单队列研究中因果推断的有力工具。本文扩展这一概念框架，以应对多队列情境中可能出现的特殊挑战。通过明确定义目标估计量，目标试验为系统评估各队列内部及数据整合过程中产生的偏倚提供了核心参照基准。基于此，研究者可针对性设计分析方法以减少偏倚，并依据潜在残留偏倚对研究结果作出合理解释。我们通过案例研究展示该框架如何通过优化分析设计与提升结果解释清晰度，从而增强多队列研究中的因果推断效力。