Longitudinal cohort studies 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 across contexts. However, the interpretation of findings can be complicated by biases that may be compounded when pooling data or may contribute to discrepant findings when analyses are replicated across cohorts. Here we extend the target trial framework, already well established as a powerful tool for causal inference in single-cohort studies, to address the specific challenges that can arise in the multi-cohort setting. The approach considers the target trial as a central point of reference, as opposed to comparing one study to another. This enables clear definition of the target estimand and systematic consideration of sources of bias within each cohort and additional sources of bias arising from data pooling. Consequently, analyses can be designed to reduce these biases and the resulting findings appropriately interpreted. We use a case study to demonstrate the approach and its potential to strengthen causal inference in multi-cohort studies through improved analysis design and clarity in the interpretation of findings.

翻译：利用多组群的数据有可能通过数据汇集来提高估计的精确度,并允许对不同背景的影响差异性进行审查,从而进一步提高估算的准确性,从而进一步增加效益;然而,如果汇集数据时出现偏差,可能会使调查结果的解释复杂化,如果将数据汇集起来,或者当分析在各组群之间复制,结果可能会造成不一致,则这种偏差可能使对调查结果的解释复杂化。在此,我们扩展了目标试验框架,在单组群研究中,作为因果关系推断的有力工具已经确立,以应对多组群设置中可能出现的具体挑战。该方法认为目标试验是一个中心参照点,而不是将一项研究进行比较。这有利于明确界定目标估计和系统审议每一组群中偏见的来源以及数据汇集中产生的其他偏差来源。因此,可以设计分析来减少这些偏差,并适当解释其结果。我们利用案例研究来展示该方法及其潜力,通过改进分析设计和解释结论的清晰度来加强多组群研究中的因果关系。