Cumulative Meta-Analysis for 'rma' Objects
The functions repeatedly fit the specified model, adding one observation/study at a time to the model.
cumul(x, ...) ## S3 method for class 'rma.uni' cumul(x, order, digits, transf, targs, progbar=FALSE, ...) ## S3 method for class 'rma.mh' cumul(x, order, digits, transf, targs, progbar=FALSE, ...) ## S3 method for class 'rma.peto' cumul(x, order, digits, transf, targs, progbar=FALSE, ...)
x |
an object of class |
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., |
targs |
optional arguments needed by the function specified under |
progbar |
logical indicating whether a progress bar should be shown (the default is |
... |
other arguments. |
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).
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 I² (only for random-effects models). |
H2 |
values of H² (only for random-effects models). |
The object is formatted 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.
When using the transf option, the transformation is applied to the estimated coefficients and the corresponding interval bounds. The standard errors are then set equal to NA and are omitted from the printed output.
Wolfgang Viechtbauer wvb@metafor-project.org http://www.metafor-project.org/
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. https://www.jstatsoft.org/v036/i03.
### 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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