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一组个体，为探究复杂暴露因素对长期健康结局的因果效应提供了机会。利用多队列数据可通过数据合并提高估计精度，并通过对各队列的分析重复检验效应异质性，从而进一步增加研究价值。然而，数据合并可能加剧偏倚，而分析重复时则可能导致结果分歧，这些偏倚往往使研究结果的解读复杂化。"目标试验"是指导单队列研究因果推断的有力工具。本文将该概念框架扩展至多队列研究中特有的挑战。通过明确界定目标估计量，目标试验为系统评估各队列及数据合并中产生的偏倚提供了核心参照基准。据此可设计减少偏倚的分析方案，并根据潜在残余偏倚对研究结果进行合理解读。我们通过案例研究展示了该框架如何通过优化分析设计和提升结果解读的清晰度来强化多队列研究的因果推断能力。