plot FixedDiscrDiscrIT: Provides plots of trial-level surrogacy in the Information-Theoretic framework

Description

Produces plots that provide a graphical representation of trial level surrogacy $R_{h t}^{2}$ based on the Information-Theoretic approach of Alonso & Molenberghs (2007).

Usage

# S3 method for FixedDiscrDiscrIT
plot(x, Weighted=TRUE, Xlab.Trial, Ylab.Trial, Main.Trial,
	 Par=par(oma=c(0, 0, 0, 0), mar=c(5.1, 4.1, 4.1, 2.1)), …)

Arguments

An object of class FixedDiscrDiscrIT.

Weighted

Logical. This argument only has effect when the user requests a trial-level surrogacy plot (i.e., when Trial.Level=TRUE in the function call). If Weighted=TRUE, the circles that depict the trial-specific treatment effects on the true endpoint against the surrogate endpoint are proportional to the number of patients in the trial. If Weighted=FALSE, all circles have the same size. Default TRUE.

Xlab.Trial

The legend of the X-axis of the plot that depicts trial-level surrogacy. Default "Treatment effect on the surrogate endpoint ( $α_{i}$ )".

Ylab.Trial

The legend of the Y-axis of the plot that depicts trial-level surrogacy. Default "Treatment effect on the true endpoint ( $β_{i}$ )".

Main.Trial

The title of the plot that depicts trial-level surrogacy. Default "Trial-level surrogacy".

Par

Graphical parameters for the plot. Default par(oma=c(0, 0, 0, 0), mar=c(5.1, 4.1, 4.1, 2.1)).

…

Extra graphical parameters to be passed to plot().

References

Alonso, A, & Molenberghs, G. (2007). Surrogate marker evaluation from an information theory perspective. Biometrics, 63, 180-186.

Examples

Run this code

# NOT RUN {
 # Time consuming (>5sec) code part
# Simulate the data:
Sim.Data.MTS(N.Total=2000, N.Trial=100, R.Trial.Target=.8, R.Indiv.Target=.8,
             Seed=123, Model="Full")
             
# create a binary true and ordinal surrogate outcome
Data.Observed.MTS$True<-findInterval(Data.Observed.MTS$True, 
        c(quantile(Data.Observed.MTS$True,0.5)))
Data.Observed.MTS$Surr<-findInterval(Data.Observed.MTS$Surr, 
        c(quantile(Data.Observed.MTS$Surr,0.333),quantile(Data.Observed.MTS$Surr,0.666)))

# Assess surrogacy based on a full fixed-effect model
# in the information-theoretic framework for a binary surrogate and ordinal true outcome:
SurEval <- FixedDiscrDiscrIT(Dataset=Data.Observed.MTS, Surr=Surr, True=True, Treat=Treat,
Trial.ID=Trial.ID, Setting="ordbin")

## Request trial-level surrogacy plot. In the trial-level plot,
## make the size of the circles proportional to the number of patients in a trial:
plot(SurEval, Weighted=FALSE)

# }

Run the code above in your browser using DataLab