Incorporating interactions into structured life course modelling approaches: A simulation study and applied example of the role of access to green space and socioeconomic position on cardiometabolic health.

In life course epidemiology, it is important to consider how multiple exposures over the lifespan may jointly influence health.We demonstrate how to extend current structured life course modelling approaches to include interactions between multiple different exposures.A simulation study comparing different methods to detect a true interaction effect found a trade-off between false positives and false negatives, suggesting that the optimal choice of method may depend on the researchers' assessment of this trade-off (e.g., exploratory studies may prefer a greater risk of false positives, while confirmatory studies may prefer to minimise the risk of false positives).We identified key factors that improve power to detect a true interaction effect, namely larger sample sizes, centering exposures, lower exposure collinearity, continuous outcomes and larger interaction effect sizes.We applied these methods in a UK birth cohort (ALSPAC; Avon Longitudinal Study of Parents and Children), finding little-to-no evidence of an association between access to green space and its interaction with socioeconomic position on child BMI, obesity or blood pressure.