rma.peto(ai, bi, ci, di, n1i, n2i,
data, slab, subset,
add=1/2, to="only0", drop00=TRUE,
level=95, digits=4, verbose=FALSE)escalc function for more details.escalc function for more details.escalc function for more details.escalc function for more details.escalc function for more details.escalc function for more details.add should be added (either "only0", "all", "if0all", or "none"). Can also be a character vector, where the first string again applies when NA). See below and the documentation FALSE).c("rma.peto","rma"). The object is a list containing the following components:print.rma.peto function. If fit statistics should also be given, use summary.rma (or use the fitstats.rma function to extract them).
The residuals.rma, rstandard.rma.peto, and rstudent.rma.peto functions extract raw and standardized residuals. Leave-one-out diagnostics can be obtained with leave1out.rma.peto.
Forest, funnel, radial, L'abbe, and Baujat plots can be obtained with forest.rma, funnel.rma, radial.rma, labbe.rma, and baujat.rma.peto. The qqnorm.rma.peto function provides normal QQ plots of the standardized residuals. One can also just call plot.rma.peto on the fitted model object to obtain various plots at once.
A cumulative meta-analysis (i.e., adding one obervation at a time) can be obtained with cumul.rma.peto.
Other assessor functions include coef.rma, vcov.rma, logLik.rma, deviance.rma, AIC.rma, and BIC.rma.ai bi n1i
group 2 ci di n2i
} where ai, bi, ci, and di denote the cell frequencies and n1i and n2i the row totals. For example, in a set of randomized clinical trials (RCTs) or cohort studies, group 1 and group 2 may refer to the treatment (exposed) and placebo/control (not exposed) group, with outcome 1 denoting some event of interest (e.g., death) and outcome 2 its complement. In a set of case-control studies, group 1 and group 2 may refer to the group of cases and the group of controls, with outcome 1 denoting, for example, exposure to some risk factor and outcome 2 non-exposure.
An approach for aggregating $2 \times 2$ table data of this type was suggested by Peto (see Yusuf et al., 1985). The method provides a weighted estimate of the (log) odds ratio under a fixed-effects model. The method is particularly advantageous when the event of interest is rare, but it should only be used when the group sizes within the individual studies are not too dissimilar and effect sizes are generally small (Greenland & Salvan, 1990; Sweeting et al., 2004; Bradburn et al., 2007). Note that the printed results are given both in terms of the log and the raw units (for easier interpretation).
Peto's method itself does not require the calculation of the individual (log) odds ratios and directly makes use of the $2 \times 2$ table counts. Zero cells are not a problem (except in extreme cases, such as when one of the two outcomes never occurs in any of the tables). Therefore, it is unnecessary to add some constant to the cell counts when there are zero cells. However, for plotting and various other functions, it is necessary to calculate the individual (log) odds ratios for the $k$ tables. Here, zero cells can be problematic, so adding a constant value to the cell counts ensures that all $k$ values can be calculated. The add and to arguments are used to specify what value should be added to the cell frequencies and under what circumstances when calculating the individual (log) odds ratios and when applying Peto's method. The documentation of the escalc function explains how the add and to arguments work. If only one value for these arguments is specified, then these values are used when calculating the individual outcomes and no adjustment to the cell counts is made when applying Peto's method. Alternatively, when specifying two values for these arguments, the first value applies when calculating the individual outcomes and the second value when applying Peto's method. Note that drop00 is set to TRUE by default, since studies where ai=ci=0 or bi=di=0 are also automatically rma.uni, rma.glmm, rma.mh, rma.mv### load data
data(dat.bcg)
### meta-analysis of the (log) odds ratios using Peto's method
rma.peto(ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)Run the code above in your browser using DataLab