Find Statistical Outliers in a Meta-Analysis
find.outliers.Rd
Searches for statistical outliers in meta-analysis results generated by meta
functions or the
rma.uni
in the metafor
package.
Arguments
- x
Either (1) an object of class
meta
, generated by themetabin
,metagen
,metacont
,metamean
,metacor
,metainc
,metarate
ormetaprop
function; or (2) and object of classrma.uni
created with therma.uni
function inmetafor
.- ...
Additional parameters for the
rma.uni
orupdate.meta
function.
Value
Returns the identified outliers and the meta-analysis results when the outliers are removed.
If the provided meta-analysis object is of class meta
, the following objects are returned if the
results of the function are saved to another object:
out.study.fixed
: A numeric vector containing the names of the outlying studies when assuming a fixed-effect model.out.study.random
: A numeric vector containing the names of the outlying studies when assuming a random-effects model. The \(\tau^{2}\) estimatormethod.tau
is inherited fromx
.m.fixed
: An object of classmeta
containing the results of the meta-analysis with outliers removed (assuming a fixed-effect model).m.random
: An object of classmeta
containing the results of the meta-analysis with outliers removed (assuming a random-effects model, and using the samemethod.tau
as in the original analysis).
If the provided meta-analysis object is of class rma.uni
, the following objects are returned if the
results of the function are saved to another object:
out.study
: A numeric vector containing the names of the outlying studies.m
: An object of classrma.uni
containing the results of the meta-analysis with outliers removed (using the same settings as in the meta-analysis object provided).
Details
This function searches for outlying studies in a meta-analysis results object. Studies are defined as outliers when their 95% confidence interval lies ouside the 95% confidence interval of the pooled effect.
When outliers are found, the function automatically recalculates the meta-analysis results, using the same settings as
in the object provided in x
, but excluding the detected outliers.
A forest plot of the meta-analysis with outliers removed can be generated directly by plugging the output of the function into
the forest
function.
References
Harrer, M., Cuijpers, P., Furukawa, T.A, & Ebert, D. D. (2019). Doing Meta-Analysis in R: A Hands-on Guide. DOI: 10.5281/zenodo.2551803. Chapter 6.2
Examples
if (FALSE) {
library(meta)
library(metafor)
library(dmetar)
# Pool with meta
m1 <- metagen(TE, seTE, data = ThirdWave,
studlab = ThirdWave$Author, common = FALSE)
# Pool with metafor
m2 <- rma(yi = TE, sei = seTE, data = ThirdWave,
slab = ThirdWave$Author, method = "PM")
# Find outliers
fo1 <- find.outliers(m1)
fo2 <- find.outliers(m2)
# Show summary
fo1
fo2
# Make forest plot
# Pass additional arguments from meta & metafor's forest function
forest(fo1, prediction = TRUE)
forest(fo2, cex = .8, col = "lightblue")
}