trimfill(x, ...)
"trimfill"(x, seTE, left=NULL, ma.fixed=TRUE, type="L", n.iter.max=50, sm=NULL, studlab=NULL, level=0.95, level.comb=level, comb.fixed=FALSE, comb.random=TRUE, hakn=FALSE, method.tau="DL", prediction=FALSE, level.predict=level, backtransf=TRUE, silent=TRUE, ...)
"trimfill"(x, left=NULL, ma.fixed=TRUE, type="L", n.iter.max=50, level=x$level, level.comb=x$level.comb, comb.fixed=FALSE, comb.random=TRUE, hakn=x$hakn, method.tau=x$method.tau, prediction=x$prediction, level.predict=x$level.predict, backtransf=x$backtransf, silent=TRUE, ...)meta, or estimated treatment
effect in individual studies.metabias(..., method="linreg")) is used to determine whether
studies are missing on the left or right side."L" or "R"."RD", "RR", "OR", "ASD",
"MD", "SMD"; ignored if x is of class
meta.x is of class meta.x$level is used as value for
level; otherwise 0.95 is used.x$level.comb is used as
value for level.comb; otherwise 0.95 is used."DL", "PM", "REML", "ML", "HS",
"SJ", "HE", or "EB", can be abbreviated.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.c("metagen", "meta", "trimfill"). The object is a
list containing the following components:
"Inverse".hakn=TRUE).x of class metabin or metacont).x of class metabin or metacont).x of class metabin).x of class metabin).x of class metacont).x of class metacont).x of class metacont).x of class metacont).x of class
metaprop).x of class
metaprop).x of class
metacor).x (e.g. 'metabin' or
'metacont'). 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 fixed effect or random effects model can be used to estimate the
number of missing studies (argument ma.fixed). Furthermore, a
fixed effect and/or random effects model can be used to summaries
study results (arguments comb.fixed and
comb.random). Simulation results (Peters et al. 2007)
indicate that the fixed-random model, i.e. using a fixed effect
model to estimate the number of missing studies and a random effects
model to summaries results, (i) performs better than the fixed-fixed
model, and (ii) performs no worse than and marginally better in
certain situations than the random-random model. Accordingly, the
fixed-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 copas.
The function metagen is called internally.
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--463.
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, funneldata(Fleiss93)
meta1 <- metabin(event.e, n.e, event.c, n.c,
data=Fleiss93, sm="OR")
tf1 <- trimfill(meta1)
summary(tf1)
funnel(tf1)
funnel(tf1, pch=ifelse(tf1$trimfill, 1, 16),
level=0.9, comb.random=FALSE)
#
# Use log odds ratios on x-axis
#
funnel(tf1, backtransf=FALSE)
funnel(tf1, pch=ifelse(tf1$trimfill, 1, 16),
level=0.9, comb.random=FALSE, backtransf=FALSE)
trimfill(meta1$TE, meta1$seTE, sm=meta1$sm)
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