A statistical method for removing unbalanced trials with multiple covariates in meta-analysis

PLoS One. 2023 Dec 15;18(12):e0295332. doi: 10.1371/journal.pone.0295332. eCollection 2023.

Abstract

In meta-analysis literature, there are several checklists describing the procedures necessary to evaluate studies from a qualitative point of view, whereas preliminary quantitative and statistical investigations on the "combinability" of trials have been neglected. Covariate balance is an important prerequisite to conduct meta-analysis. We propose a method to identify unbalanced trials with respect to a set of covariates, in presence of covariate imbalance, namely when the randomized controlled trials generate a meta-sample that cannot satisfy the requisite of randomization/combinability in meta-analysis. The method is able to identify the unbalanced trials, through four stages aimed at achieving combinability. The studies responsible for the imbalance are identified, and then they can be eliminated. The proposed procedure is simple and relies on the combined Anderson-Darling test applied to the Empirical Cumulative Distribution Functions of both experimental and control meta-arms. To illustrate the method in practice, two datasets from well-known meta-analyses in the literature are used.

Publication types

  • Meta-Analysis

MeSH terms

  • Clinical Protocols
  • Computer Simulation
  • Research Design*

Grants and funding

The research was supported by grants from Italian Ministerial grant PRIN 2017 “From high school to job placement: micro-data life course analysis of university student mobility and its impact on the Italian North-South divide.”, n. 2017HBTK5P, of which MA is Principal Investigator. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.