metafor (version 4.8-0)

cumul: Cumulative Meta-Analysis for 'rma' Objects

Description

Function to carry out a ‘cumulative meta-analysis’, by repeatedly fitting the specified model adding one study at a time.

Usage

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

Value

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

k

number of studies included in the analysis.

estimate

estimated (average) outcomes.

se

corresponding standard errors.

zval

corresponding test statistics.

pval

corresponding p-values.

ci.lb

lower bounds of the confidence intervals.

ci.ub

upper bounds of the confidence intervals.

Q

test statistics for the test of heterogeneity.

Qp

corresponding p-values.

tau2

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

I2

values of I^2.

H2

values of H^2.

order

values of the order variable (if specified).

...

other arguments.

When the model was fitted with test="t", test="knha", test="hksj", or test="adhoc", then zval is called tval in the object that is returned by the function.

The object is formatted and printed with the print function. To format the results as a data frame, one can use the as.data.frame function. A forest plot showing the results from the cumulative meta-analysis can be obtained with forest. Alternatively, plot can also be used to visualize the results.

Arguments

x

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

order

optional argument to specify a variable based on which the studies will be ordered for the cumulative meta-analysis.

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 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.

collapse

logical to specify whether studies with the same value of the order variable should be added simultaneously (the default is FALSE).

progbar

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

...

other arguments.

Author

Wolfgang Viechtbauer (, https://www.metafor-project.org).

Details

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

If argument order is not specified, the studies are added according to their order in the original dataset.

When a variable is specified for order, the variable is assumed to be of the same length as the original dataset that was used in the model fitting (and if the data argument was used in the original model fit, then the variable will be searched for within this data frame first). Any subsetting and removal of studies with missing values that was applied during the model fitting is also automatically applied to the variable specified via the order argument.

By default, studies are added one at a time. However, if a variable is specified for the order argument and collapse=TRUE, then studies with the same value of the order variable are added simultaneously.

References

Chalmers, T. C., & Lau, J. (1993). Meta-analytic stimulus for changes in clinical trials. Statistical Methods in Medical Research, 2(2), 161--172. https://doi.org/10.1177/096228029300200204

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(1), 45--57. https://doi.org/10.1016/0895-4356(94)00106-z

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

forest for a function to draw cumulative forest plots and plot for a different visualization of the cumulative results.

Examples

### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg,
              data=dat.bcg, slab=paste0(author, ", ", year))

### fit random-effects model
res <- rma(yi, vi, data=dat, digits=3)

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

### add studies with the same publication year simultaneously
cumul(res, order=year, transf=exp, collapse=TRUE)

### 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, slab=paste0(author, ", ", year), digits=3)

### cumulative meta-analysis
cumul(res, order=year)
cumul(res, order=year, transf=exp)

### add studies with the same publication year simultaneously
cumul(res, order=year, transf=exp, collapse=TRUE)

### meta-analysis of the (log) odds ratios using Peto's method
res <- rma.peto(ai=tpos, bi=tneg, ci=cpos, di=cneg,
                data=dat.bcg, slab=paste0(author, ", ", year), digits=3)

### cumulative meta-analysis
cumul(res, order=year)
cumul(res, order=year, transf=exp)

### add studies with the same publication year simultaneously
cumul(res, order=year, transf=exp, collapse=TRUE)

### make the first log risk ratio missing and fit the model without study 2;
### then the variable specified via 'order' should still be of the same length
### as the original dataset; subsetting and removal of studies with missing
### values is automatically done by the cumul() function
dat$yi[1] <- NA
res <- rma(yi, vi, data=dat, subset=-2, digits=3)
cumul(res, transf=exp, order=year)