metafor (version 2.0-0)

cumul: Cumulative Meta-Analysis for 'rma' Objects

Description

The functions repeatedly fit the specified model, adding one observation/study at a time to the model.

Usage

cumul(x, …)
# S3 method for rma.uni
cumul(x, order, digits, transf, targs, progbar=FALSE, …)
# S3 method for rma.mh
cumul(x, order, digits, transf, targs, progbar=FALSE, …)
# S3 method for rma.peto
cumul(x, order, digits, transf, targs, progbar=FALSE, …)

Arguments

x

an object of class "rma.mh", "rma.peto", "rma.uni".

order

optional vector with indices giving the desired order for the cumulative meta-analysis.

digits

integer specifying 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 specifying the name of a function that should be used to transform the model coefficients and 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.

progbar

logical indicating whether a progress bar should be shown (the default is FALSE).

…

other arguments.

Value

An object of class c("list.rma","cumul.rma"). The object is a list containing the following components:

estimate

estimated coefficients of the model.

se

standard errors of the coefficients.

zval

test statistics of the coefficients.

pval

p-values for the test statistics.

ci.lb

lower bounds of the confidence intervals for the coefficients.

ci.ub

upper bounds of the confidence intervals for the coefficients.

QE

test statistics for the tests of heterogeneity.

QEp

p-values for the tests of heterogeneity.

tau2

estimated amounts of (residual) heterogeneity (only for random-effects models).

I2

values of \(I<U+00B2>\) (only for random-effects models).

H2

values of \(H<U+00B2>\) (only for random-effects models).

The object is formated and printed with print.list.rma. A forest plot showing the results from the cumulative meta-analysis can be obtained with forest.cumul.rma. For random-effects models, plot.cumul.rma can also be used to visualize the results.

Details

For "rma.uni" objects, the model specified by x must be a model without moderators (i.e., either a fixed- or a random-effects model).

References

Chalmers, T. C., & Lau, J. (1993). Meta-analytic stimulus for changes in clinical trials. Statistical Methods in Medical Research, 2, 161--172.

Lau, J., Schmid, C. H., & Chalmers, T. C. (1995). Cumulative meta-analysis of clinical trials builds evidence for exemplary medical care. Journal of Clinical Epidemiology, 48, 45--57.

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1--48. http://www.jstatsoft.org/v36/i03/.

See Also

forest.cumul.rma, plot.cumul.rma

Examples

# NOT RUN {
### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)

### random-effects model
res <- rma(yi, vi, data=dat)

### cumulative meta-analysis (in the order of publication year)
cumul(res, transf=exp, order=order(dat$year))

### meta-analysis of the (log) risk ratios using the Mantel-Haenszel method
res <- rma.mh(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)

### cumulative meta-analysis
cumul(res, order=order(dat.bcg$year))
cumul(res, order=order(dat.bcg$year), transf=TRUE)

### meta-analysis of the (log) odds ratios using Peto's method
res <- rma.mh(ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)

### cumulative meta-analysis
cumul(res, order=order(dat.bcg$year))
cumul(res, order=order(dat.bcg$year), transf=TRUE)
# }

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