A Hierarchical Meta-Analysis for Settings Involving Multiple Outcomes across Multiple Cohorts

TL;DR

This paper introduces a hierarchical meta-analysis method to synthesize data from multiple cohorts, assessing the impact of prenatal alcohol exposure on various cognitive outcomes and estimating a global effect.

Contribution

It develops a novel hierarchical meta-analytic framework that integrates multiple endpoints and cohorts, accounting for correlations and confounders in dose-response analysis.

Findings

Estimated dose-response effects of PAE on cognition across cohorts

Quantified the global impact of prenatal alcohol exposure on cognitive development

Compared hierarchical meta-analysis with full multivariate approaches

Abstract

Evidence from animal models and epidemiological studies has linked prenatal alcohol exposure (PAE) to a broad range of long-term cognitive and behavioral deficits. However, there is virtually no information in the scientific literature regarding the levels of PAE associated with an increased risk of clinically significant adverse effects. During the period from 1975-1993, several prospective longitudinal cohort studies were conducted in the U.S., in which maternal reports regarding alcohol use were obtained during pregnancy and the cognitive development of the offspring was assessed from early childhood through early adulthood. The sample sizes in these cohorts did not provide sufficient power to examine effects associated with different levels and patterns of PAE. To address this critical public health issue, we have developed a hierarchical meta-analysis to synthesize information regarding the effects of PAE on cognition, integrating data on multiple endpoints from six U.S. longitudinal cohort studies. Our approach involves estimating the dose-response coefficients for each endpoint and then pooling these correlated dose-response coefficients to obtain an estimated `global' effect of exposure on cognition. In the first stage, we use individual participant data to derive estimates of the effects of PAE by fitting regression models that adjust for potential confounding variables using propensity scores. The correlation matrix characterizing the dependence between the endpoint-specific dose-response coefficients estimated within each cohort is then run, while accommodating incomplete information on some endpoints. We also compare and discuss inferences based on the proposed approach to inferences based on a full multivariate analysis