metafor (version 3.0-2)

regtest: Regression Test for Funnel Plot Asymmetry


The function can be used to carry out (various versions of) Egger's regression test for funnel plot asymmetry.


regtest(x, …)

# S3 method for rma regtest(x, model="rma", predictor="sei",, digits, …)

# S3 method for default regtest(x, vi, sei, ni, subset, model="rma", predictor="sei",, digits, …)



an object of class "rma" or a vector with the observed effect sizes or outcomes.


vector with the corresponding sampling variances (needed if x is a vector with the observed effect sizes or outcomes).


vector with the corresponding standard errors (note: only one of the two, vi or sei, needs to be specified).


vector with the corresponding sample sizes.


optional (logical or numeric) vector to specify the subset of studies that should be included in the test. Only relevant when passing a vector via x.


either "rma" or "lm" to indicate the type of model to use for the regression test. See ‘Details’.


either "sei" "vi", "ni", "ninv", "sqrtni", or "sqrtninv" to indicate the predictor to use for the regression test. See ‘Details’.

logical to specify whether the full results from the fitted model should also be returned.


integer to specify the number of decimal places to which the printed results should be rounded (the default is to take the value from the object or is 4 for the default method).

other arguments.


An object of class "regtest". The object is a list containing the following components:


the model used for the regression test.


the predictor used for the regression test.


the value of the test statistic.


the corresponding p-value


the degrees of freedom of the test statistic (if the test is based on a t-distribution).


the full results from the fitted model.


the limit estimate (only for predictors "sei" "vi", "ninv", or "sqrtninv" and when the model does not contain any additional moderators; NULL otherwise)

lower bound of the confidence interval for the limit estimate.


upper bound of the confidence intervals for the limit estimate.

The results are formatted and printed with the print.regtest function.


Various tests for funnel plot asymmetry have been suggested in the literature, including the rank correlation test by Begg and Mazumdar (1994) and the regression test by Egger et al. (1997). Extensions, modifications, and further developments of the regression test are described (among others) by Macaskill, Walter, and Irwig (2001), Sterne and Egger (2005), Harbord, Egger, and Sterne (2006), Peters et al. (2006), R<U+00FC>cker et al. (2008), and Moreno et al. (2009). The various versions of the regression test differ in terms of the model (either a weighted regression model with a multiplicative dispersion term or a fixed/random/mixed-effects meta-regression model is used), in terms of the predictor variable that the observed effect sizes or outcomes are hypothesized to be related to when publication bias is present (suggested predictors include the standard error, the sampling variance, and the sample size or transformations thereof), and in terms of the outcome measure used (e.g., for 2 22x2 table data, one has the choice between various outcome measures). The idea behind the various tests is the same though: If there is a relationship between the observed effect sizes or outcomes and the chosen predictor, then this usually implies asymmetry in the funnel plot, which in turn may be an indication of publication bias.

The regtest function can be used to carry out various versions of the regression test. The model is chosen via the model argument, with model="lm" for weighted regression with a multiplicative dispersion term or model="rma" for the meta-analytic models. In the latter case, arguments such as method, weighted, and test used during the initial model fitting are also used for the regression test. Therefore, if one wants to conduct the regression test with a random/mixed-effects model, one should first fit a random-effects model with the rma function and then use the regtest function on the fitted model object.

The predictor is chosen via the predictor argument:

  • predictor="sei" for the standard error,

  • predictor="vi" for the sampling variance,

  • predictor="ni" for the sample size,

  • predictor="ninv" for the inverse of the sample size,

  • predictor="sqrtni" for the square root transformed sample size, and

  • predictor="sqrtninv" for the inverse of the square root transformed sample size.

For predictors based on the sample size, the object x obviously must contain the information about the sample sizes. This will automatically be the case when measure was not equal to "GEN" or the ni values were explicitly specified during the initial model fitting.

If the model passed to the regtest function already included one or more moderators, then regtest will add the chosen predictor to the moderator(s) already included in the model. This way, one can test for funnel plot asymmetry after accounting first for the influence of the moderator(s) already included.

One can also pass a vector with the observed effect sizes or outcomes (via x) and the corresponding sampling variances via vi (or the standard errors via sei) directly to the function (in this case, the regtest.default function is used). When the predictor is the sample size or a transformation thereof, then ni needs to be specified here as well.

The outcome measure used for the regression test is simply determined by what measure was used in fitting the original model (or what values are passed to regtest.default).

The model used for conducting the regression test can also be used to obtain a ‘limit estimate’ of the (average) true effect or outcome. In particular, when the standard error, sampling variance, or inverse (square root) sample size is used as the predictor, the model intercept in essence reflects the estimate under infinite precision. This is sometimes (cautiously) interpreted as an estimate of the (average) true effect or outcome that is adjusted for publication bias.


Begg, C. B., & Mazumdar, M. (1994). Operating characteristics of a rank correlation test for publication bias. Biometrics, 50(4), 1088--1101.

Egger, M., Davey Smith, G., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. British Medical Journal, 315(7109), 629--634.

Harbord, R. M., Egger, M., & Sterne, J. A. C. (2006). A modified test for small-study effects in meta-analyses of controlled trials with binary endpoints. Statistics in Medicine, 25(20), 3443--3457.

Macaskill, P., Walter, S. D., & Irwig, L. (2001). A comparison of methods to detect publication bias in meta-analysis. Statistics in Medicine, 20(4), 641--654.

Moreno, S. G., Sutton, A. J., Ades, A. E., Stanley, T. D., Abrams, K. R., Peters, J. L., & Cooper, N. J. (2009). Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study. BMC Medical Research Methodology, 9, 2.

Peters, J. L., Sutton, A. J., Jones, D. R., Abrams, K. R., & Rushton, L. (2006). Comparison of two methods to detect publication bias in meta-analysis. Journal of the American Medical Association, 295(6), 676--680.

R<U+00FC>cker, G., Schwarzer, G., & Carpenter, J. (2008). Arcsine test for publication bias in meta-analyses with binary outcomes. Statistics in Medicine, 27(5), 746--763.

Stanley, T. D., & Doucouliagos, H. (2014). Meta-regression approximations to reduce publication selection bias. Research Synthesis Methods, 5(1), 60--78.

Sterne, J. A. C., & Egger, M. (2005). Regression methods to detect publication and other bias in meta-analysis. In H. R. Rothstein, A. J. Sutton, & M. Borenstein (Eds.) Publication bias in meta-analysis: Prevention, assessment, and adjustments (pp. 99--110). Chichester, England: Wiley.

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1--48.

See Also



Run this code
### copy data into 'dat' and examine data
dat <- dat.egger2001

### calculate log odds ratios and corresponding sampling variances (but remove ISIS-4 trial)
dat <- escalc(measure="OR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, subset=-16)

### fit random-effects model
res <- rma(yi, vi, data=dat)

### classical Egger test
regtest(res, model="lm")

### random/mixed-effects version of the Egger test

### same tests, but passing outcomes directly
regtest(dat$yi, dat$vi, model="lm")
regtest(dat$yi, dat$vi)

### examples using the sample size (or a transformation thereof) as predictor
regtest(res, model="lm", predictor="ni")
regtest(res, model="lm", predictor="ninv")
regtest(res, model="rma", predictor="ni")
regtest(res, model="rma", predictor="ninv")

### if dat$yi is computed with escalc(), sample size information is stored in attributes

### then this will work
regtest(dat$yi, dat$vi, predictor="ni")

### otherwise have to supply sample sizes manually
dat$ni <- with(dat, n1i + n2i)
dat$yi <- c(dat$yi) # this removes the 'ni' attribute from 'yi'
regtest(dat$yi, dat$vi, ni=dat$ni, predictor="ni")

### standard funnel plot (with standard error on y-axis)
funnel(res, refline=0)

### regression test (by default the standard error is used as predictor)
reg <- regtest(res)

### add regression line to funnel plot
se <- seq(0,1.8,length=100)
lines(coef(reg$fit)[1] + coef(reg$fit)[2]*se, se, lwd=2)

### regression test (using the sampling variance as predictor)
reg <- regtest(res, predictor="vi")

### add regression line to funnel plot (using the sampling variance as predictor)
lines(coef(reg$fit)[1] + coef(reg$fit)[2]*se^2, se, lwd=2)

### testing for asymmetry after accounting for the influence of a moderator
res <- rma(yi, vi, mods = ~ year, data=dat)
regtest(res, model="lm")
# }

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