SROC: A standard summary receiver operating chracteristic plot.

In meta4diag: Meta-Analysis for Diagnostic Test Studies

A standard summary receiver operating chracteristic plot.

Description

Takes a meta4diag object produced by meta4diag() and makes the standard summary receiver operating chracteristic plot.

Usage

SROC(x,...)
## S3 method for class 'meta4diag'
SROC(x, sroc.type=1, est.type="mean", sp.cex=1.5,sp.pch=8,sp.col="red",
              dataShow="o", data.col="#FF0000", data.cex="scaled", data.pch=1, 
              lineShow=T, line.lty=1, line.lwd=2, line.col="black",
              crShow=T, cr.lty=2, cr.lwd=1.5, cr.col="blue",
              prShow=T, pr.lty=3, pr.lwd=1,  pr.col="darkgray",
              dataFit = T, add=FALSE, main="", xlim, ylim,
              legend=F,legend.cex = 0.7,...)

Arguments

x	A meta4diag object.
sroc.type	A numerical value specifying the function used to make SROC line. Options are 1, 2, 3, 4, 5. When sroc.type=1, the SROC line is plotted as " The regression line 1" according to Arends et al.(2008), y = \mu + \rho\sqrt{\frac{\sigma_{\mu}^2}{\sigma_{\nu}^2}}(x-\nu) When sroc.type=2, the SROC line is plotted as "The major axis method", y = \frac{\sigma_{\mu}^2-\sigma_{\nu}^2\pm\sqrt{(\sigma_{\mu}^2-\sigma_{\nu}^2)^2+4\rho^2\sigma_{\mu}^2\sigma_{\nu}^2}}{2\rho\sqrt{\sigma_{\mu}^2\sigma_{\nu}^2}}(x-\nu)+\mu When sroc.type=3, the SROC line is plotted as "The Moses and Littenberg's regression line", y = \frac{\sigma_{\mu}^2 + \rho\sqrt{\sigma_{\mu}^2\sigma_{\nu}^2}}{\sigma_{\nu}^2+\rho\sqrt{\sigma_{\mu}^2\sigma_{\nu}^2}}(x-\nu)+\mu When sroc.type=4, the SROC line is plotted as "The regression line 2", y = \mu + \frac{1}{\rho}\sqrt(\frac{\sigma_{\mu}^2}{\sigma_{\nu}^2})(x-\nu) When sroc.type=5, the SROC line is plotted as "The Rutter and Gatsonis's SROC curve", y = \mu + \sqrt{\frac{\sigma_{\mu}^2}{\sigma_{\nu}^2}}(x-\nu)
est.type	The estimates type used to make SROC plot. Options are "mean" and "median".
sp.cex	Summary points size. The summary points are mean or median of sensitivities and specificities of all sudies.
sp.pch	Point symbols of summary points.
sp.col	Color of summary points.
dataShow	A character indicating whether the original dataset or the fitted dataset is shown or not. If is "o", the original data will be plotted. If is "f", the fitted data will be plotted. No dataset will be plotted if dataShow is not "o" or "f".
data.col	Color of orignal data bubbles.
data.cex	A string or a numerical value indicating the size of the plotted dataset points. If is "bubble" or "scaled", the size of the data points are proportional to the total number of individuals in each study.
data.pch	A string or a numerical value indicating the symbol of the plotted dataset points.
lineShow	Boolean indicating whether the SROC line is shown or not.
line.lty	SROC line type.
line.lwd	SROC line width.
line.col	Color for the SROC line.
crShow	Boolean indicating whether the confidence region is shown or not.
cr.lty	Confidence region line width.
cr.lwd	Confidence region line width.
cr.col	Color for the confidence region line.
prShow	Boolean indicating whether the prediction region is shown or not.
pr.lty	Prediction region line type.
pr.lwd	Prediciton region line width.
pr.col	Color for the prediction region line.
dataFit	Boolean indicating the length SROC line. Either plotted from -1 to 1, or fit the data.
add	If add is TRUE, the plots are added to an existing plot, otherwise a new plot is created.
main	A overall title for the plot.
xlim	A numeric value, giving the x coordinates ranges.
ylim	A numeric value, giving the y coordinates ranges.
legend	Boolean indicating whether the legend is shown or not. Can also be a string indicating the position to show the legend. Options are "left", "bottom" and "right".
legend.cex	Lengend size.
...	Further arguments passed to or from other methods.

Details

The SROC() returns a plot of the true positive rate against the false positive rate (1-specificity) (ROC space) for the different possible cutpoints of a diagnostic test. It shows the tradeoff between sensitivity and specificity (any increase in sensitivity will be accompanied by a decrease in specificity). The closer the curve follows the left-hand border and then the top border of the ROC space, the more accurate the test. The closer the curve comes to the 45-degree diagonal of the ROC space, the less accurate the test. The area under the curve is a measure of text accuracy. Summary points, summary receiver operating characteristic line, confidence region and prediction region of the summary points are plotted.

The function SROC() gives the user flexibility to control the plots.

Value

The return value is a list of the files created (if any).

Author(s)

Jingyi Guo and Andrea Riebler

References

Chappell F, Raab G, Wardlaw J (2009). "When are Summary ROC Curves Appropriate for Diagnostic Meta-analyses?" Statistics in Medicine, 28(21), 2653-2668.

Arends et al.(2008) "Bivariate random effects meta-analysis of ROC curves." Medical Decision Making, 28(5), 621-638.

See Also

meta4diag

Examples

## Not run: 
if(requireNamespace("INLA", quietly = TRUE)){
  require("INLA", quietly = TRUE)
  data(Catheter)
  res <- meta4diag(data = Catheter)
  SROC(res, est.type="mean")
}

## End(Not run)

meta4diag documentation built on Nov. 29, 2023, 3:01 a.m.