  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.
