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A hierarchical meta-analysis for settings involving multiple outcomes across multiple cohorts
Tugba Akkaya Hocagil1, Louise M Ryan2, Richard J Cook1
1Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
Stat (International Statistical Institute)
|October 16, 2023
Summary
Prenatal alcohol exposure (PAE) can cause cognitive deficits. This study developed a new meta-analysis method to better estimate PAE
Area of Science:
- Neuroscience
- Epidemiology
- Biostatistics
Background:
- Prenatal alcohol exposure (PAE) is linked to cognitive and behavioral deficits.
- Limited data exists on PAE levels causing clinically significant cognitive issues.
Purpose of the Study:
- To develop a robust hierarchical meta-analysis method to estimate PAE effects on cognition.
- To synthesize data from multiple outcomes across six U.S. longitudinal cohort studies.
Main Methods:
- Developed a hierarchical meta-analysis approach to handle correlated outcomes.
- Estimated dose-response coefficients for each outcome and pooled them for a global effect.
- Used individual participant data with propensity score adjustment and addressed incomplete information.
Main Results:
- The new method provides robust estimates of PAE's cognitive effects.
- Addressed the violation of independence assumption in standard meta-analyses with multiple outcomes.
- Compared the proposed approach with full multivariate analysis.
Conclusions:
- The hierarchical meta-analysis approach effectively synthesizes correlated cognitive outcomes from PAE studies.
- This method offers a more accurate understanding of the dose-response relationship between PAE and cognitive function.
- Provides a foundation for future research on neurodevelopmental outcomes following PAE.
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