Trim-and-fill method for estimating and adjusting for the number and outcomes of missing studies in a meta-analysis.
# S3 method for 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"),
...
)# S3 method for default
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"),
backtransf = TRUE,
pscale = 1,
irscale = 1,
irunit = "person-years",
silent = TRUE,
...
)
An object of class c("trimfill", "metagen", "meta") with
corresponding generic functions (see meta-object).
An object of class meta, or estimated treatment
effect in individual studies.
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 metabias(..., method="Egger")) is used to
determine whether studies are missing on the left or right side.
A logical indicating whether a common effect or random effects model is used to estimate the number of missing studies.
A character indicating which method is used to estimate
the number of missing studies. Either "L" or "R".
Maximum number of iterations to estimate number of missing studies.
A logical indicating whether a common effect meta-analysis should be conducted.
A logical indicating whether a random effects meta-analysis should be conducted.
A logical indicating whether a prediction interval should be printed.
A logical indicating whether results should be
back transformed in printouts and plots. If
backtransf=TRUE, results for sm="OR" are printed as
odds ratios rather than log odds ratios and results for
sm="ZCOR" are printed as correlations rather than Fisher's
z transformed correlations, for example.
A numeric giving scaling factor for printing of
single event probabilities or risk differences, i.e. if argument
sm is equal to "PLOGIT", "PLN",
"PRAW", "PAS", "PFT", or "RD".
A numeric defining a scaling factor for printing of
single incidence rates or incidence rate differences, i.e. if
argument sm is equal to "IR", "IRLN",
"IRS", "IRFT", or "IRD".
A character specifying the time unit used to calculate rates, e.g. person-years.
A logical indicating whether basic information on iterations shown.
A logical indicating whether warnings should be printed if deprecated arguments are used.
Additional arguments (to catch deprecated arguments).
Standard error of estimated treatment effect.
An optional character string indicating underlying
summary measure, e.g., "RD", "RR", "OR",
"ASD", "HR", "MD", "SMD", or
"ROM"; ignored if x is of class meta.
An optional vector with study labels; ignored if
x is of class meta.
The level used to calculate confidence intervals for
individual studies. If existing, x$level is used as value
for level; otherwise 0.95 is used.
The level used to calculate confidence interval for
the pooled estimate. If existing, x$level.ma is used as
value for level.ma; otherwise 0.95 is used.
A character string indicating which method
is used to calculate confidence interval and test statistic for
random effects estimate (see meta-package).
A character string indicating whether an
ad hoc variance correction should be applied in the case
of an arbitrarily small Hartung-Knapp variance estimate (see
meta-package).
A character string indicating which method is
used to estimate the between-study variance \(\tau^2\) and its
square root \(\tau\) (see meta-package).
A character string indicating which method is
used to estimate the confidence interval of \(\tau^2\) and
\(\tau\) (see meta-package).
The level used to calculate prediction interval for a new study.
A character string indicating which method is
used to calculate a prediction interval (see
meta-package).
A character string indicating whether an
ad hoc variance correction should be applied for the
prediction interval (see meta-package).
Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de
The trim-and-fill method (Duval, Tweedie 2000a, 2000b) can be used for estimating and adjusting for the number and outcomes of missing studies in a meta-analysis. 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
R-estimator) 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 common-random 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 common-common
model, and (ii) performs no worse than and marginally better in
certain situations than the random-random model. Accordingly, the
common-random model is the default.
An empirical comparison of the trim-and-fill method and the Copas selection model (Schwarzer et al. 2010) indicates that the trim-and-fill method leads to excessively conservative inference in practice. The Copas selection model is available in R package metasens.
The function metagen is called internally.
Duval S & Tweedie R (2000a): A nonparametric "Trim and Fill" method of accounting for publication bias in meta-analysis. Journal of the American Statistical Association, 95, 89--98
Duval S & Tweedie R (2000b): Trim and Fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. 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 between-study heterogeneity. Statisics in Medicine, 10, 4544--62
Schwarzer G, Carpenter J, Rücker G (2010): Empirical evaluation suggests Copas selection model preferable to trim-and-fill method for selection bias in meta-analysis 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$trimfill, 1, 16), level = 0.9, random = FALSE)
#
# Use log odds ratios on x-axis
#
funnel(tf1, backtransf = FALSE)
funnel(tf1, pch = ifelse(tf1$trimfill, 1, 16), level = 0.9, random = FALSE,
backtransf = FALSE)
trimfill(m1$TE, m1$seTE, sm = m1$sm)
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