Causal inference in multi-cohort studies using the target trial approach

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.