# Outliers in meta-analysis: an asymmetric trimmed-mean approach

## TL;DR

This paper introduces an asymmetric trimmed-mean method for meta-analysis that effectively down-weights outliers without removing data, using a modified bootstrap approach, and demonstrates its robustness through empirical and simulation studies.

## Contribution

It adapts the asymmetric trimmed mean with a modified bootstrap for meta-analysis, providing a non-parametric way to handle outliers.

## Key findings

- Down-weights outliers effectively in real datasets
- Maintains performance when no outliers are present
- Does not rely on parametric assumptions about outliers

## Abstract

The adaptive asymmetric trimmed mean is a known way of estimating central location, usually in conjunction with the bootstrap. It is here modified and applied to meta-analysis, as a way of dealing with outlying results by down-weighting the corresponding studies. This requires a modified bootstrap and a method of down-weighting studies, as opposed to removing single observations. This methodology is shown in analysis of some well-travelled datasets to down-weight outliers in agreement with other methods, and Monte-Carlo studies show that it does does not appreciably down-weight studies when outliers are absent. Conceptually simple, it does not make parametric assumptions about the outliers.