metamisc (version 0.1.9)

plot.valmeta: Forest Plots

Description

Function to create forest plots for objects of class "valmeta".

Usage

# S3 method for valmeta
plot(x, sort = "asc", ...)

Arguments

x

An object of class "valmeta"

sort

By default, studies are ordered by ascending effect size (sort="asc"). For study ordering by descending effect size, choose sort="desc". For any other value, study ordering is ignored.

…

Additional arguments which are passed to forest.

Value

An object of class ggplot

Details

The forest plot shows the performance estimates of each validation with corresponding confidence intervals. A polygon is added to the bottom of the forest plot, showing the summary estimate based on the model. A 95% prediction interval is added by default for random-effects models, the dotted line indicates its (approximate) bounds.

References

• Debray TPA, Damen JAAG, Snell KIE, Ensor J, Hooft L, Reitsma JB, et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ. 2017;356:i6460.

• Lewis S, Clarke M. Forest plots: trying to see the wood and the trees. BMJ. 2001; 322(7300):1479--80.

• Riley RD, Higgins JPT, Deeks JJ. Interpretation of random effects meta-analyses. BMJ. 2011 342:d549--d549.

Examples

# NOT RUN {
data(EuroSCORE)
fit <- with(EuroSCORE, valmeta(cstat=c.index, cstat.se=se.c.index, 
            cstat.95CI=cbind(c.index.95CIl,c.index.95CIu), N=n, O=n.events))
plot(fit)

library(ggplot2)
plot(fit, theme=theme_grey())

# }