metabias {meta} | R Documentation |
Test for funnel plot asymmetry, based on rank correlation or linear regression method.
metabias(x, seTE, TE.fixed, seTE.fixed, method = "rank", plotit = FALSE, correct = FALSE)
x |
An object of class meta or estimated treatment
effect in individual studies. |
seTE |
Standard error of estimated treatment effect (mandatory if
x not of class meta ). |
TE.fixed |
Overall treatment estimate (mandatory if x not
of class meta and method = "rank" ). |
seTE.fixed |
Standard error of overall treatment estimate
(mandatory if x not of class meta and method =
"rank" ). |
method |
A character string indicating which test is to be
used. Either "rank" , "linreg" , "mm" ,
"count" , "score" , or "peters" , can be
abbreviated. |
plotit |
A logical indicating whether a plot should be produced
for method "rank" , "linreg" , "mm" , or
"score" . |
correct |
A logical indicating whether a continuity corrected
statistic is used for rank correlation methods "rank" and "count" . |
If method
is "rank"
, the test statistic is based on the
rank correlation between standardised treatment estimates and variance
estimates of estimated treatment effects; Kendall's tau is used as
correlation measure (Begg & Mazumdar, 1994). The test statistic
follows a standard normal distribution. By default (if correct
is FALSE), no continuity correction is utilised (Kendall & Gibbons,
1990).
If method
is "linreg"
, the test statistic is based on a
weighted linear regression of the treatment effect on its standard
error (Egger et al., 1997). The test statistic follows a t
distribution with number of studies - 2
degrees of freedom.
If method
is "mm"
, the test statistic is based on a
weighted linear regression of the treatment effect on its standard
error using the method of moments estimator for the additive
between-study variance component (method 3a in Thompson, Sharp,
1999). The test statistic follows a t distribution with number
of studies - 2
degrees of freedom.
If method
is "count"
, the test statistic is based on
the rank correlation between a standardised cell frequency and the
inverse of the variance of the cell frequency; Kendall's tau is used
as correlation measure (Schwarzer et al., 2007). The test statistic
follows a standard normal distribution. By default (if correct
is FALSE), no continuity correction is utilised (Kendall & Gibbons,
1990).
If method
is "score"
, the test statistic is based on a
weighted linear regression utilising efficient score and score
variance (Harbord et al., 2006). The test statistic follows a t
distribution with number of studies - 2
degrees of freedom.
If method
is "peters"
, the test statistic is based on a
weighted linear regression of the treatment effect on the inverse of
the total sample size using the variance of the average event rate as
weights (Peters et al., 2006). The test statistic follows a t
distribution with number of studies - 2
degrees of freedom.
In order to calculate an arcsine test for funnel plot asymmetry
(Ruecker et al., 2008), one has to use the metabin
function
with parameter sm="AS"
as input to the metabias
command. The three arcsine tests described in Ruecker et al. (2008)
can be calculated by setting method
to "rank"
,
"linreg"
and "mm"
, respectively.
A list with class "htest"
containing the following components:
estimate |
The estimated degree of funnel plot asymmetry, with
name "ks" or "bias" corresponding to the method
employed, i.e., rank correlation or regression method. |
statistic |
The value of the test statistic. |
parameter |
The degrees of freedom of the test statistic in the case that it follows a t distribution. |
p.value |
The p-value for the test. |
alternative |
A character string describing the alternative hypothesis. |
method |
A character string indicating what type of test was used. |
data.name |
A character string giving the names of the data. |
Guido Schwarzer sc@imbi.uni-freiburg.de
Begg CB & Mazumdar M (1994), Operating characteristics of a rank correlation test for publication bias. Biometrics, 50, 1088–1101.
Egger M, Smith GD, Schneider M & Minder C (1997), Bias in meta-analysis detected by a simple, graphical test. British Medical Journal, 315, 629–634.
Harbord RM, Egger M & Sterne J (2006), A modified test for small-study effects in meta-analyses of controlled trials with binary endpoints. Statistics in Medicine, 25, 3443–3457.
Kendall M & Gibbons JD (1990), Rank Correlation Methods. London: Edward Arnold.
Peters JL, Sutton AJ, Jones DR, Abrams KR & Rushton L (2006), Comparison of two methods to detect publication bias in meta-analysis. Journal of the American Medical Association, 295, 676–680.
Ruecker G, Schwarzer G, Carpenter JR (2008) Arcsine test for publication bias in meta-analyses with binary outcomes. Statistics in Medicine, 27,746–763.
Schwarzer G, Antes G & Schumacher M (2007), A test for publication bias in meta-analysis with sparse binary data. Statistics in Medicine, 26, 721–733.
Thompson SG & Sharp, SJ (1999), Explaining heterogeneity in meta-analysis: A comparison of methods, Statistics in Medicine, 18, 2693–2708.
funnel
, metabin
, metacont
, metagen
data(Olkin95) meta1 <- metabin(event.e, n.e, event.c, n.c, data=Olkin95, subset=c(41,47,51,59), sm="RR", meth="I") metabias(meta1) metabias(meta1, correct=TRUE) metabias(meta1, method="linreg") metabias(meta1, method="linreg", plotit=TRUE) metabias(meta1, method="count") ## ## Same result: ## metabias(meta1, method="linreg")$p.value metabias(meta1$TE, meta1$seTE, method="linreg")$p.value ## ## Arcsine test: ## meta1.as <- metabin(event.e, n.e, event.c, n.c, data=Olkin95, subset=c(41,47,51,59), sm="AS", meth="I") metabias(meta1.as, method="linreg")