Plot.FixedDiscrDiscrIT: Provides plots of trial-level surrogacy in the...

In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

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

Description

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

Usage

 ## S3 method for class '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

x	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 (\alpha_{i})".
Ylab.Trial	The legend of the Y-axis of the plot that depicts trial-level surrogacy. Default "Treatment effect on the true endpoint (\beta_{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().

Author(s)

Hannah M. Ensor & Christopher J. Weir

References

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

See Also

FixedDiscrDiscrIT

Examples

## 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)


## End(Not run)

Surrogate documentation built on Sept. 25, 2023, 5:07 p.m.