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.Comment: 34 pages, 3 figure