trimfill.meta: Trim and fill method for meta-analysis

Description

Trim and fill method for estimating and adjusting for the number and outcomes of missing studies in a meta-analysis.

Usage

## S3 method for class 'default':
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,
         silent=TRUE, ...)
## S3 method for class 'meta':
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,
         silent=TRUE, ...)

Arguments

An object of class meta, or estimated treatment effect in individual studies.

seTE

Standard error of estimated treatment effect.

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 metabias(..., method="linreg")) is used to

ma.fixed

A logical indicating whether a fixed 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 "L" or "R".

n.iter.max

Maximum number of iterations to estimate number of missing studies.

An optional character string indicating underlying summary measure, e.g., "RD", "RR", "OR", "AS", "MD", "SMD"; ignored if x is of class meta

studlab

An optional vector with study labels; ignored if x is of class meta.

level

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.

level.comb

The level used to calculate confidence interval for the pooled estimate. If existing, x$level.comb is used as value for level.comb; otherwise 0.95 is used.

comb.fixed

A logical indicating whether a fixed effect meta-analysis should be conducted.

comb.random

A logical indicating whether a random effects meta-analysis should be conducted.

hakn

A logical indicating whether the method by Hartung and Knapp should be used to adjust test statistics and confidence intervals.

method.tau

A character string indicating which method is used to estimate the between-study variance tau-squared. Either "DL", "REML", "ML", "HS", "SJ", "HE", or "EB"

prediction

A logical indicating whether a prediction interval should be printed.

level.predict

The level used to calculate prediction interval for a new study.

silent

A logical indicating whether basic information on iterations shown.

...

other arguments

Value

An object of class c("metagen", "meta", "trimfill"). The object is a list containing the following components:
studlab, sm, left, ma.fixed, type,
n.iter.max, level, level.comb, level.predict,As defined above.
comb.fixed, comb.random, prediction, hakn, method.tau
TE, seTEEstimated treatment effect and standard error of individual studies.
w.fixed, w.randomWeight of individual studies (in fixed and random effects model).
TE.fixed, seTE.fixedEstimated overall treatment effect and standard error (fixed effect model).
TE.random, seTE.randomEstimated overall treatment effect and standard error (random effects model).
seTE.predictStandard error utilised for prediction interval.
lower.predict, upper.predictLower and upper limits of prediction interval.
kNumber of studies combined in meta-analysis.
QHeterogeneity statistic Q.
tauSquare-root of between-study variance.
methodPooling method: "Inverse".
callFunction call.
n.iterActual number of iterations to estimate number of missing studies.
trimfillA logical vector indicating studies that have been added by trim and fill method.
df.haknDegrees of freedom for test of treatment effect for Hartung-Knapp method (only if hakn=TRUE).
titleTitle of meta-analysis / systematic review.
complabComparison label.
outclabOutcome label.
label.eLabel for experimental group.
label.cLabel for control group.
label.leftGraph label on left side of forest plot.
label.rightGraph label on right side of forest plot.
k0Number of studies added by trim and fill.
n.eNumber of observations in experimental group (only for object x of class metabin or metacont).
n.cNumber of observations in control group (only for object x of class metabin or metacont).
event.eNumber of events in experimental group (only for object x of class metabin).
event.cNumber of events in control group (only for object x of class metabin).
mean.eEstimated mean in experimental group (only for object x of class metacont).
sd.eStandard deviation in experimental group (only for object x of class metacont).
mean.cEstimated mean in control group (only for object x of class metacont).
sd.cStandard deviation in control group (only for object x of class metacont).
nNumber of observations (only for object x of class metaprop).
eventNumber of events (only for object x of class metaprop).
corCorelation (only for object x of class metacor).
class.xMain class of object x (e.g. 'metabin' or 'metacont').
versionVersion of R package meta used to create object.

Details

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 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.

References

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--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.

Examples

Run this code

data(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)

trimfill(meta1$TE, meta1$seTE, sm=meta1$sm)

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