Meta-analysis on studies with heterogeneous and partially observed covariates

Tugba Akkaya Hocagil; Hon Hwang; Joseph L Jacobson; Sandra W Jacobson; Louise M Ryan

doi:10.11124/JBIES-23-00078

Meta-analysis on studies with heterogeneous and partially observed covariates

JBI Evid Synth. 2024 Mar 1;22(3):413-433. doi: 10.11124/JBIES-23-00078.

Authors

Tugba Akkaya Hocagil^{1

2}, Hon Hwang³, Joseph L Jacobson⁴, Sandra W Jacobson⁴, Louise M Ryan^{3

5}

Affiliations

¹ Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.
² Department of Biostatistics, Ankara University School of Medicine, Ankara, Turkiye.
³ School of Mathematical and Physical Sciences, University of Technology Sydney, Sydney, NSW, Australia.
⁴ Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, USA.
⁵ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

PMID: 38475899
DOI: 10.11124/JBIES-23-00078

Abstract

Individual participant data meta-analysis is a commonly used alternative to the traditional aggregate data meta-analysis. It is popular because it avoids relying on published results and enables direct adjustment for relevant covariates. However, a practical challenge is that the studies being combined often vary in terms of the potential confounders that were measured. Furthermore, it will inevitably be the case that some individuals have missing values for some of those covariates. In this paper, we demonstrate how these challenges can be resolved using a propensity score approach, combined with multiple imputation, as a strategy to adjust for covariates in the context of individual participant data meta-analysis. To illustrate, we analyze data from the Bill and Melinda Gates Foundation-funded Healthy Birth, Growth, and Development Knowledge Integration project to investigate the relationship between physical growth rate in the first year of life and cognition measured later during childhood. We found that the overall effect of average growth velocity on cognitive outcome is slightly, but significantly, positive with an estimated effect size of 0.36 (95% CI 0.18, 0.55).