densCurves: Density curves plot

In optimalThreshold: Bayesian Methods for Optimal Threshold Estimation

Description

This function plots the density curves of the marker values in each treatment arm (treatment selection marker), or for diseased and non-diseased patients (diagnostic marker).

Usage

densCurves(x0, x1, type, ylab = "Density", xlab = "Marker values",
  main = "Density curves", col0 = "blue", col1 = "green", lty0 = 1,
  lty1 = 1, pos.legend = "topright", ...)

Arguments

x0	a numeric vector containing the marker values of patients in the reference arm (treatment selection marker), or non-diseased patients (diagnostic marker).
x1	a numeric vector containing the marker values of patients in the innovative arm (treatment selection marker), or diseased patients (diagnostic marker).
type	a character argument that specifies the type of the marker ("treatment selection" for treatment selection marker or "diagnostic" for diagnostic marker).
ylab	label of the Y-axis.
xlab	label of the X-axis.
main	title of the graph.
col0	color of the density curve in the reference arm (treatment selection marker), or for non-diseased patients (diagnostic marker).
col1	color of the density curve in the innovative arm (treatment selection marker), or for diseased patients (diagnostic marker).
lty0	type of the line for the density curve in the reference arm (treatment selection marker), or for non-diseased patients (diagnostic marker).
lty1	type of the line for the density curve in the innovative arm (treatment selection marker), or for diseased patients (diagnostic marker).
pos.legend	the x and y co-ordinates to be used to position the legend. They can be specified by keyword or in any way which is accepted by xy.coords.
...	other arguments to be passed to the plot function.

Details

When assessing treatment selection markers and estimating their optimal threshold, it is necessary that the randomization constraint be respected. If the density curves of the marker are different when comparing the two treatment arms, then it is likely that the trtSelThresh function will provide a threshold that do not correspond to the true optimal threshold. When assessing diagnostic markers, it is necessary to define the decision rule (classically high values of the marker are associated with a worst outcome). This decision rule may be checked with the density curves of the marker for diseased and non-diseased patients.

Value

None

Examples

### Plotting density curves for a treatment selection marker
# Data generation
x0E <- rnorm(100, 2, 1)
x0Eb <- rnorm(200, 4, 1)
x1E <- rnorm(100, 4, 1)
x1Eb <- rnorm(200, 2, 1)
densCurves(x0 = c(x0E, x0Eb), x1 = c(x1E, x1Eb), type = "treatment selection")

### Plotting density curves for a diagnostic marker
# Data generation
xE <- rnorm(30, 3, 1)
xEb <- rnorm(90, 1, 1)
densCurves(x0 = xEb, x1 = xE, type = "diagnostic")

optimalThreshold documentation built on Jan. 13, 2020, 5:06 p.m.