metafor (version 3.8-1)

hc: Meta-Analysis based on the Method by Henmi and Copas (2010)

Description

The function can be used to obtain an estimate of the average true outcome and corresponding confidence interval under a random-effects model using the method described by Henmi and Copas (2010).

Usage

hc(object, ...)
# S3 method for rma.uni
hc(object, digits, transf, targs, control, ...)

Value

An object of class "hc.rma.uni". The object is a list containing the following components:

beta

estimated average true outcome.

se

corresponding standard error.

ci.lb

lower bound of the confidence intervals for the average true outcome.

ci.ub

upper bound of the confidence intervals for the average true outcome.

...

some additional elements/values.

The results are formatted and printed with the print function.

Arguments

object

an object of class "rma.uni".

digits

optional integer to specify the number of decimal places to which the printed results should be rounded. If unspecified, the default is to take the value from the object.

transf

optional argument to specify a function to transform the estimate and the corresponding interval bounds (e.g., transf=exp; see also transf). If unspecified, no transformation is used.

targs

optional arguments needed by the function specified under transf.

control

list of control values for the iterative algorithm. If unspecified, default values are defined inside the function. See ‘Note’.

...

other arguments.

Author

Original code by Henmi and Copas (2010). Corrected for typos by Michael Dewey (). Incorporated into the package with some small adjustments for consistency with the other functions in the package by Wolfgang Viechtbauer ().

Details

The model specified via object must be a model without moderators (i.e., either an equal- or a random-effects model).

When using the usual method for fitting a random-effects model (i.e., weighted estimation with inverse-variance weights), the weights assigned to smaller and larger studies become more uniform as the amount of heterogeneity increases. As a consequence, the estimated average outcome could become increasingly biased under certain forms of publication bias (where smaller studies on one side of the funnel plot are missing). The method by Henmi and Copas (2010) tries to counteract this problem by providing an estimate of the average true outcome that is based on inverse-variance weights as used under an equal-effects model (which do not take the amount of heterogeneity into consideration). The amount of heterogeneity is still estimated (with the DerSimonian-Laird estimator) and incorporated into the standard error of the estimated average outcome and the corresponding confidence interval.

Currently, there is only a method for handling objects of class "rma.uni" with the hc function. It therefore provides a method for conducting a sensitivity analysis after the model has been fitted with the rma.uni function.

References

Henmi, M., & Copas, J. B. (2010). Confidence intervals for random effects meta-analysis and robustness to publication bias. Statistics in Medicine, 29(29), 2969--2983. https://doi.org/10.1002/sim.4029

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

rma.uni for the function to fit rma.uni models.

Examples

### calculate log odds ratios and corresponding sampling variances
dat <- escalc(measure="OR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat.lee2004)
dat

### meta-analysis based on log odds ratios
res <- rma(yi, vi, data=dat)
res

### funnel plot as in Henmi and Copas (2010)
funnel(res, yaxis="seinv", refline=0, xlim=c(-3,3), ylim=c(.5,3.5), steps=7, digits=1, back="white")

### use method by Henmi and Copas (2010) as a sensitivity analysis
hc(res)

### back-transform results to odds ratio scale
hc(res, transf=exp)