optimalThreshold (version 1.0)

riskCurves: Marker-by-treatment predictiveness curves plot

Description

This function plots the marker-by-treatment predictiveness curves for treatment selection markers, corresponding to the risk of event in each treatment arm in function of the cumulative distribution of the marker.

Usage

riskCurves(x0E, x0Eb, x1E, x1Eb, ylab = "Predicted risk of event",
  xlab = "Empirical cumulative distribution function of the marker",
  main = "Marker-by-treatment predictiveness curves")

Arguments

x0E

a numeric vector of the marker values for patients in the reference arm that developed the event.

x0Eb

a numeric vector of the marker values for patients in the reference arm that did not develop the event.

x1E

a numeric vector of the marker values for patients in the innovative arm that developed the event.

x1Eb

a numeric vector of the marker values for patients in the innovative arm that did not develop the event.

ylab

label of the Y-axis.

xlab

label of the X-axis.

main

title of the graph.

Value

None

Details

The function uses regression splines to plot the marker-by-treatment predictiveness curves. This graph may be used to check graphically the strength of the marker-by-treatment interaction, and to know whether low values of the marker are associated with a better response of the reference treatment (this information is needed in the trtSelThresh function).

References

Janes, H, Pepe, MS, Bossuyt, PM, and Barlow, WE. Measuring the performance of markers for guiding treatment decisions. Annals of Internal Medicine. 2011; 154(4): 253-259.

See Also

gam for more details about regression splines.

Examples

# NOT RUN {
x0E <- rnorm(100, 2, 1)
x0Eb <- rnorm(100, 4, 1)
x1E <- rnorm(100, 4, 1)
x1Eb <- rnorm(100, 2, 1)
riskCurves(x0E, x0Eb, x1E, x1Eb)
# }