optimalThreshold: riskCurves – R documentation

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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.

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).

Value

None

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.

Examples

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)

optimalThreshold

Bayesian Methods for Optimal Threshold Estimation

v1.0

GPL (>= 2.0)

Authors

Yoann Blangero [aut, cre]

Initial release

2020-01-09