rma.peto(ai, bi, ci, di, n1i, n2i,
data, slab, subset,
add=c(1/2,0), to=c("only0","none"),
level=95, digits=4)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 "all", "only0", "if0all", or "none"). The first string again applies when calculating the indc("rma.peto","rma"). The object is a list containing the following components:print.rma.peto function. If you also want the fit statistics, 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, and L'abbe plots can be obtained with forest.rma, funnel.rma, radial.rma, and labbe.rma. 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 2x2 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. Note that the printed results are given both in terms of the log and the raw units (for easier interpretation).
The method itself does not require the calculation of the individual (log) odds ratios and directly makes use of the 2x2 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 usually 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 2x2 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 should be used. The first value of the add and to arguments applies when calculating the individual outcomes, the second value when applying Peto's method.rma.uni, rma.mh### load BCG vaccine 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