Direct and mediated effects (DME) SLCMA: a novel method for life course modelling with time-varying covariates

Background

In prospective cohort studies, where an exposure is collected repeatedly, interest often lies in determining whether the timing of that exposure has a differential effect on a later outcome. The Structured Life Course Modeling Approach (SLCMA), where users select between temporal hypotheses of exposure specified a priori, provides one way to analyse such longitudinal data. However, few studies using SLCMA consider the effect of time-varying covariates (TVC) which may impact associations.

Methods

We present a modified version of the SLCMA – called direct and mediated effects (DME)-SLCMA – which corrects for TVC. We first develop the DME-SLCMA method, test it through simulation, and apply it to psychosocial data from the Drakenstein Child Health Study (DCHS, n =336) to investigate relationships between maternal psychopathology, TVC of socioeconomic status, and offspring depressive symptoms.

Results

We found that, on average, offspring depressive symptoms score increased by 3.9% (95% CI: 1.0%-6.9%, p = 0.039) for each unit of maternal psychopathology (SRQ) at 48 months whilst adjusting for time-varying socioeconomic status (at 18, 30, 42 and 54 months). Our simulations identified several realistic scenarios where selections ignoring TVC - with TVC mediated exposure effects present - were prone to be incorrect, including our DCHS example.

Conclusion

DME-SLCMA is a robust new approach for life course modelling in the presence of time-varying covariates. We recommend adjusting for TVC whenever possible, and, when not possible, our simulation study identified that scenarios where mediated effects are comparable, or greater, in magnitude to direct effects are most prone to confounding.

Key messages

• We extended the structured life course modelling approach (SLCMA), where a hypothesis is selected from a set defined a priori, to adjust for time-varying covariates.

• Through a thorough simulation study, we found the new method is robust and unbiased.

• The method reveals the importance of adjusting for time-varying covariates when mediated effects from exposures to the outcome are large compared with direct effects; adjustment thus prevents confounding and incorrect hypothesis conclusions.

• In an application investigating the association between the timing of exposure to maternal psychopathology and offspring depression, whilst adjusting for time-varying socioeconomic status, using DME SLCMA we selected a critical period at 48 months as best explaining offspring depressive symptoms.