trimfill.meta  R Documentation 
Trimandfill method for estimating and adjusting for the number and outcomes of missing studies in a metaanalysis.
## S3 method for class 'meta'
trimfill(
x,
left = NULL,
ma.common = TRUE,
type = "L",
n.iter.max = 50,
common = FALSE,
random = TRUE,
prediction = x$prediction,
backtransf = x$backtransf,
pscale = x$pscale,
irscale = x$irscale,
irunit = x$irunit,
silent = TRUE,
warn.deprecated = gs("warn.deprecated"),
...
)
## Default S3 method:
trimfill(
x,
seTE,
left = NULL,
ma.common = TRUE,
type = "L",
n.iter.max = 50,
sm = "",
studlab = NULL,
level = 0.95,
level.ma = level,
common = FALSE,
random = TRUE,
method.random.ci = gs("method.random.ci"),
adhoc.hakn.ci = gs("adhoc.hakn.ci"),
method.tau = gs("method.tau"),
method.tau.ci = if (method.tau == "DL") "J" else "QP",
prediction = FALSE,
level.predict = level,
method.predict = gs("method.predict"),
adhoc.hakn.pi = gs("adhoc.hakn.pi"),
seed.predict = NULL,
backtransf = TRUE,
pscale = 1,
irscale = 1,
irunit = "personyears",
silent = TRUE,
...
)
x 
An object of class 
left 
A logical indicating whether studies are supposed to be
missing on the left or right side of the funnel plot. If NULL,
the linear regression test for funnel plot symmetry (i.e.,
function 
ma.common 
A logical indicating whether a common effect or random effects model is used to estimate the number of missing studies. 
type 
A character indicating which method is used to estimate
the number of missing studies. Either 
n.iter.max 
Maximum number of iterations to estimate number of missing studies. 
common 
A logical indicating whether a common effect metaanalysis should be conducted. 
random 
A logical indicating whether a random effects metaanalysis should be conducted. 
prediction 
A logical indicating whether a prediction interval should be printed. 
backtransf 
A logical indicating whether results should be
back transformed in printouts and plots. If

pscale 
A numeric giving scaling factor for printing of
single event probabilities or risk differences, i.e. if argument

irscale 
A numeric defining a scaling factor for printing of
single incidence rates or incidence rate differences, i.e. if
argument 
irunit 
A character specifying the time unit used to calculate rates, e.g. personyears. 
silent 
A logical indicating whether basic information on iterations shown. 
warn.deprecated 
A logical indicating whether warnings should be printed if deprecated arguments are used. 
... 
Additional arguments (to catch deprecated arguments). 
seTE 
Standard error of estimated treatment effect. 
sm 
An optional character string indicating underlying
summary measure, e.g., 
studlab 
An optional vector with study labels; ignored if

level 
The level used to calculate confidence intervals for
individual studies. If existing, 
level.ma 
The level used to calculate confidence interval for
the pooled estimate. If existing, 
method.random.ci 
A character string indicating which method
is used to calculate confidence interval and test statistic for
random effects estimate (see 
adhoc.hakn.ci 
A character string indicating whether an
ad hoc variance correction should be applied in the case
of an arbitrarily small HartungKnapp variance estimate (see

method.tau 
A character string indicating which method is
used to estimate the betweenstudy variance 
method.tau.ci 
A character string indicating which method is
used to estimate the confidence interval of 
level.predict 
The level used to calculate prediction interval for a new study. 
method.predict 
A character string indicating which method is
used to calculate a prediction interval (see

adhoc.hakn.pi 
A character string indicating whether an
ad hoc variance correction should be applied for the
prediction interval (see 
seed.predict 
A numeric value used as seed to calculate
bootstrap prediction interval (see 
The trimandfill method (Duval, Tweedie 2000a, 2000b) can be used for estimating and adjusting for the number and outcomes of missing studies in a metaanalysis. The method relies on scrutiny of one side of a funnel plot for asymmetry assumed due to publication bias.
Three different methods have been proposed originally to estimate
the number of missing studies. Two of these methods (L and
Restimator) have been shown to perform better in simulations, and
are available in this R function (argument type
).
A common effect or random effects model can be used to estimate the
number of missing studies (argument ma.common
). Furthermore,
a common effect and/or random effects model can be used to
summaries study results (arguments common
and
random
). Simulation results (Peters et al. 2007) indicate
that the commonrandom model, i.e. using a common effect model to
estimate the number of missing studies and a random effects model
to summaries results, (i) performs better than the commoncommon
model, and (ii) performs no worse than and marginally better in
certain situations than the randomrandom model. Accordingly, the
commonrandom model is the default.
An empirical comparison of the trimandfill method and the Copas selection model (Schwarzer et al. 2010) indicates that the trimandfill method leads to excessively conservative inference in practice. The Copas selection model is available in R package metasens.
The function metagen
is called internally.
An object of class c("trimfill", "metagen", "meta")
with
corresponding generic functions (see metaobject
).
Guido Schwarzer guido.schwarzer@uniklinikfreiburg.de
Duval S & Tweedie R (2000a): A nonparametric "Trim and Fill" method of accounting for publication bias in metaanalysis. Journal of the American Statistical Association, 95, 89–98
Duval S & Tweedie R (2000b): Trim and Fill: A simple funnelplotbased method of testing and adjusting for publication bias in metaanalysis. Biometrics, 56, 455–63
Peters JL, Sutton AJ, Jones DR, Abrams KR, Rushton L (2007): Performance of the trim and fill method in the presence of publication bias and betweenstudy heterogeneity. Statisics in Medicine, 10, 4544–62
Schwarzer G, Carpenter J, Rücker G (2010): Empirical evaluation suggests Copas selection model preferable to trimandfill method for selection bias in metaanalysis Journal of Clinical Epidemiology, 63, 282–8
metagen
, metabias
,
funnel
data(Fleiss1993bin)
m1 < metabin(d.asp, n.asp, d.plac, n.plac, data = Fleiss1993bin, sm = "OR")
tf1 < trimfill(m1)
tf1
funnel(tf1)
funnel(tf1, pch = ifelse(tf1$trimfill, 1, 16), level = 0.9, random = FALSE)
#
# Use log odds ratios on xaxis
#
funnel(tf1, backtransf = FALSE)
funnel(tf1, pch = ifelse(tf1$trimfill, 1, 16), level = 0.9, random = FALSE,
backtransf = FALSE)
trimfill(m1$TE, m1$seTE, sm = m1$sm)
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