The function can be used to carry out the rank correlation test for funnel plot asymmetry.
ranktest(x, …) # S3 method for rma ranktest(x, digits, …) # S3 method for default ranktest(x, vi, sei, subset, digits, …)
an object of class
vector with the corresponding sampling variances.
vector with the corresponding standard errors.
optional vector indicating the subset of studies that should be included in the test. This can be a logical vector of the same length as
integer specifying the number of decimal places to which the printed results should be rounded (the default is 4).
The function carries out the rank correlation test as described by Begg and Mazumdar (1994). The test can be used to examine whether the observed outcomes and the corresponding sampling variances are correlated. A high correlation would indicate that the funnel plot is asymmetric, which may be a result of publication bias.
One can either pass an object of class
"rma" to the function or a vector of observed effect sizes or outcomes (via
x) and the corresponding sampling variances via
vi (or the standard errors via
An object of class
"ranktest.rma". The object is a list containing the following components:
the estimated value of Kendall's tau rank correlation coefficient
the corresponding p-value for the test that the true tau is equal to zero
The method does not depend on the model fitted. Therefore, regardless of the model passed to the function, the results of the rank test will always be the same. See
regtest for tests of funnel plot asymmetry that are based on regression models and model dependent.
The function makes use of the
cor.test function with
method="kendall". If possible, an exact p-value is provided; otherwise, a large-sample approximation is used.
Begg, C. B., & Mazumdar, M. (1994). Operating characteristics of a rank correlation test for publication bias. Biometrics, 50, 1088--1101.
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1--48. http://www.jstatsoft.org/v36/i03/.
### calculate log risk ratios and corresponding sampling variances dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg) ### random-effects model res <- rma(yi, vi, data=dat) ranktest(res)#> #> Rank Correlation Test for Funnel Plot Asymmetry #> #> Kendall's tau = 0.0256, p = 0.9524 #>ranktest(dat$yi, dat$vi)#> #> Rank Correlation Test for Funnel Plot Asymmetry #> #> Kendall's tau = 0.0256, p = 0.9524 #>