# The importance of considering variability in re-expression of effect estimates for use in meta-analyses

## TL;DR

This paper critiques a recent study's conclusion that re-expression methods are unreliable for meta-analyses by highlighting issues with ignoring variability in effect estimates.

## Contribution

The paper identifies and corrects a methodological flaw in treating effect estimates as constants rather than random variables.

## Key findings

- Treating effect estimates as constants led to misleading conclusions about re-expression methods.
- Re-expression methods can be appropriate for small to moderate sample sizes when variability is considered.
- The critique suggests refinements are needed for more accurate estimation of point effects.

## Abstract

Comparing and combining reports from different publication is of interest to many when conducting meta-analyses. However, challenges can arise with reports using transformations of the exposure data. A recent publication, Linakis et al. (BMC Med Res Methodol 24:6, 2024), compared methods for re-expression with the conclusion that the re-expression methods examined are not reliable. In their analysis, they treated the estimated effect estimates, which are random variables, as if they were constants, which have no inherent variability. This letter describes two places where this assumption was made and how it affected their conclusions. While the re-expression methods demonstrate potential room for refinement in terms of estimating the observed point estimate, with the statistically appropriate consideration of variability, use of re-expression for small to moderate sample sizes (up to approximately 5000) seems appropriate. That contrasts with the author’s conclusion that use of re-expression methods is not suitable for meta-analyses.

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Source: https://tomesphere.com/paper/PMC12573819