trimfill: Trim and Fill Analysis for 'rma.uni' Objects
In metafor: Meta-Analysis Package for R
trimfill R Documentation
Trim and Fill Analysis for 'rma.uni' Objects
Description
Function to carry out a trim and fill analysis for objects of class "rma.uni". \loadmathjax
Usage
trimfill(x, ...)
## S3 method for class 'rma.uni'
trimfill(x, side, estimator="L0", maxiter=100, verbose=FALSE, ilim, ...)
Arguments
x
an object of class "rma.uni".
side
optional character string (either "left" or "right") to specify on which side of the funnel plot the missing studies should be imputed. If left unspecified, the side is chosen within the function depending on the results of the regression test (see regtest for details on this test).
estimator
character string (either "L0", "R0", or "Q0") to specify the estimator for the number of missing studies (the default is "L0").
maxiter
integer to specify the maximum number of iterations for the trim and fill method (the default is 100).
verbose
logical to specify whether output should be generated on the progress of the iterative algorithm used as part of the trim and fill method (the default is FALSE).
ilim
limits for the imputed values. If unspecified, no limits are used.
...
other arguments.
Details
The trim and fill method is a nonparametric (rank-based) data augmentation technique proposed by Duval and Tweedie (2000a, 2000b; see also Duval, 2005). The method can be used to estimate the number of studies missing from a meta-analysis due to suppression of the most extreme results on one side of the funnel plot. The method then augments the observed data so that the funnel plot is more symmetric and recomputes the summary estimate based on the complete data. The trim and fill method can only be used in the context of an equal- or a random-effects model (i.e., in models without moderators). The method should not be regarded as a way of yielding a more ‘valid’ estimate of the overall effect or outcome, but as a way of examining the sensitivity of the results to one particular selection mechanism (i.e., one particular form of publication bias).
Value
An object of class c("rma.uni.trimfill","rma.uni","rma"). The object is a list containing the same components as objects created by rma.uni, except that the data are augmented by the trim and fill method. The following components are also added:
k0
estimated number of missing studies.
side
either "left" or "right", indicating on which side of the funnel plot the missing studies (if any) were imputed.
se.k0
standard error of k0.
p.k0
p-value for the test of \mjeqn\mboxH_0H_0: no missing studies on the chosen side (only when estimator="R0"; NA otherwise).
yi
the observed effect sizes or outcomes plus the imputed values (if there are any).
vi
the corresponding sampling variances
fill
a logical vector indicating which of the values in yi are the observed (FALSE) and the imputed (TRUE) data.
The results of the fitted model after the data augmentation are printed with the print function. Calling funnel on the object provides a funnel plot of the observed and imputed data.
Note
Three different estimators for the number of missing studies were proposed by Duval and Tweedie (2000a, 2000b). Based on these articles and Duval (2005), "R0" and "L0" are recommended. An advantage of estimator "R0" is that it provides a test of the null hypothesis that the number of missing studies (on the chosen side) is zero.
If the outcome measure used for the analysis is bounded (e.g., correlations are bounded between -1 and +1, proportions are bounded between 0 and 1), one can use the ilim argument to enforce those limits when imputing values (imputed values cannot exceed those bounds then).
The model used during the trim and fill procedure is the same as used by the original model object. Hence, if an equal-effects model is passed to the function, then an equal-effects model is also used during the trim and fill procedure and the results provided are also based on an equal-effects model. This would be an ‘equal-equal’ approach. Similarly, if a random-effects model is passed to the function, then the same model is used as part of the trim and fill procedure and for the final analysis. This would be a ‘random-random’ approach. However, one can also easily fit a different model for the final analysis than was used for the trim and fill procedure. See ‘Examples’ for an illustration of an ‘equal-random’ approach.
Author(s)
Wolfgang Viechtbauer wvb@metafor-project.org https://www.metafor-project.org
References
Duval, S. J., & Tweedie, R. L. (2000a). Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics, 56(2), 455–463. https://doi.org/10.1111/j.0006-341x.2000.00455.x
Duval, S. J., & Tweedie, R. L. (2000b). A nonparametric "trim and fill" method of accounting for publication bias in meta-analysis. Journal of the American Statistical Association, 95(449), 89–98. https://doi.org/10.1080/01621459.2000.10473905
Duval, S. J. (2005). The trim and fill method. In H. R. Rothstein, A. J. Sutton, & M. Borenstein (Eds.) Publication bias in meta-analysis: Prevention, assessment, and adjustments (pp. 127–144). Chichester, England: Wiley.
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03
See Also
funnel for a function to create funnel plots of the observed and augmented data.
regtest for the regression test, ranktest for the rank correlation test, tes for the test of excess significance, fsn to compute the fail-safe N (file drawer analysis), and selmodel for selection models.
Examples
### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)
### meta-analysis of the log risk ratios using an equal-effects model
res <- rma(yi, vi, data=dat, method="EE")
taf <- trimfill(res)
taf
funnel(taf, cex=1.2, legend=list(show="cis"))
### estimator "R0" also provides test of H0: no missing studies (on the chosen side)
taf <- trimfill(res, estimator="R0")
taf
### meta-analysis of the log risk ratios using a random-effects model
res <- rma(yi, vi, data=dat)
taf <- trimfill(res)
taf
funnel(taf, cex=1.2, legend=list(show="cis"))
### the examples above are equal-equal and random-random approaches
### illustration of an equal-random approach
res <- rma(yi, vi, data=dat, method="EE")
taf <- trimfill(res)
filled <- data.frame(yi = taf$yi, vi = taf$vi, fill = taf$fill)
filled
rma(yi, vi, data=filled)
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| trimfill | R Documentation |
Trim and Fill Analysis for 'rma.uni' Objects
Description
Function to carry out a trim and fill analysis for objects of class "rma.uni". \loadmathjax
Usage
trimfill(x, ...)
## S3 method for class 'rma.uni'
trimfill(x, side, estimator="L0", maxiter=100, verbose=FALSE, ilim, ...)
Arguments
x |
an object of class |
side |
optional character string (either |
estimator |
character string (either |
maxiter |
integer to specify the maximum number of iterations for the trim and fill method (the default is |
verbose |
logical to specify whether output should be generated on the progress of the iterative algorithm used as part of the trim and fill method (the default is |
ilim |
limits for the imputed values. If unspecified, no limits are used. |
... |
other arguments. |
Details
The trim and fill method is a nonparametric (rank-based) data augmentation technique proposed by Duval and Tweedie (2000a, 2000b; see also Duval, 2005). The method can be used to estimate the number of studies missing from a meta-analysis due to suppression of the most extreme results on one side of the funnel plot. The method then augments the observed data so that the funnel plot is more symmetric and recomputes the summary estimate based on the complete data. The trim and fill method can only be used in the context of an equal- or a random-effects model (i.e., in models without moderators). The method should not be regarded as a way of yielding a more ‘valid’ estimate of the overall effect or outcome, but as a way of examining the sensitivity of the results to one particular selection mechanism (i.e., one particular form of publication bias).
Value
An object of class c("rma.uni.trimfill","rma.uni","rma"). The object is a list containing the same components as objects created by rma.uni, except that the data are augmented by the trim and fill method. The following components are also added:
k0 |
estimated number of missing studies. |
side |
either |
se.k0 |
standard error of k0. |
p.k0 |
p-value for the test of \mjeqn\mboxH_0H_0: no missing studies on the chosen side (only when |
yi |
the observed effect sizes or outcomes plus the imputed values (if there are any). |
vi |
the corresponding sampling variances |
fill |
a logical vector indicating which of the values in |
The results of the fitted model after the data augmentation are printed with the print function. Calling funnel on the object provides a funnel plot of the observed and imputed data.
Note
Three different estimators for the number of missing studies were proposed by Duval and Tweedie (2000a, 2000b). Based on these articles and Duval (2005), "R0" and "L0" are recommended. An advantage of estimator "R0" is that it provides a test of the null hypothesis that the number of missing studies (on the chosen side) is zero.
If the outcome measure used for the analysis is bounded (e.g., correlations are bounded between -1 and +1, proportions are bounded between 0 and 1), one can use the ilim argument to enforce those limits when imputing values (imputed values cannot exceed those bounds then).
The model used during the trim and fill procedure is the same as used by the original model object. Hence, if an equal-effects model is passed to the function, then an equal-effects model is also used during the trim and fill procedure and the results provided are also based on an equal-effects model. This would be an ‘equal-equal’ approach. Similarly, if a random-effects model is passed to the function, then the same model is used as part of the trim and fill procedure and for the final analysis. This would be a ‘random-random’ approach. However, one can also easily fit a different model for the final analysis than was used for the trim and fill procedure. See ‘Examples’ for an illustration of an ‘equal-random’ approach.
Author(s)
Wolfgang Viechtbauer wvb@metafor-project.org https://www.metafor-project.org
References
Duval, S. J., & Tweedie, R. L. (2000a). Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics, 56(2), 455–463. https://doi.org/10.1111/j.0006-341x.2000.00455.x
Duval, S. J., & Tweedie, R. L. (2000b). A nonparametric "trim and fill" method of accounting for publication bias in meta-analysis. Journal of the American Statistical Association, 95(449), 89–98. https://doi.org/10.1080/01621459.2000.10473905
Duval, S. J. (2005). The trim and fill method. In H. R. Rothstein, A. J. Sutton, & M. Borenstein (Eds.) Publication bias in meta-analysis: Prevention, assessment, and adjustments (pp. 127–144). Chichester, England: Wiley.
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03
See Also
funnel for a function to create funnel plots of the observed and augmented data.
regtest for the regression test, ranktest for the rank correlation test, tes for the test of excess significance, fsn to compute the fail-safe N (file drawer analysis), and selmodel for selection models.
Examples
### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)
### meta-analysis of the log risk ratios using an equal-effects model
res <- rma(yi, vi, data=dat, method="EE")
taf <- trimfill(res)
taf
funnel(taf, cex=1.2, legend=list(show="cis"))
### estimator "R0" also provides test of H0: no missing studies (on the chosen side)
taf <- trimfill(res, estimator="R0")
taf
### meta-analysis of the log risk ratios using a random-effects model
res <- rma(yi, vi, data=dat)
taf <- trimfill(res)
taf
funnel(taf, cex=1.2, legend=list(show="cis"))
### the examples above are equal-equal and random-random approaches
### illustration of an equal-random approach
res <- rma(yi, vi, data=dat, method="EE")
taf <- trimfill(res)
filled <- data.frame(yi = taf$yi, vi = taf$vi, fill = taf$fill)
filled
rma(yi, vi, data=filled)
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