Structural equation models for meta‐analysis in environmental risk assessment

Abstract

The potential of structural equation models for combining information from different studies in environmental epidemiology is explored. For illustration we synthesize data from two birth cohorts assessing the effects of prenatal exposure to methylmercury on childhood cognitive performance. One cohort was the largest by far, but a smaller cohort included superior assessment of the PCB exposure which has been considered an important confounder when estimating the mercury effect. The data were analyzed by specification of a structural equation model for each cohort. Information was then pooled based on a joint likelihood function with key parameters constrained to be equal in the different models. Modeling assumptions were chosen to obtain a meaningful biological interpretation of the joint effect parameters. Measurement errors in mercury variables were taken into account by viewing observed variables as indicators of latent variables. Adjustments for measurement error were also included for confounder variables. In particular, this example illustrates how to properly utilize that one study provided superior information about a confounder. A final more advanced model pooled information across different outcomes to gain power and to avoid multiple testing problems. In this model, the mercury effect remained statistically significant, while the effect of PCB was less certain.