Surrogate (version 3.3.1)

plot PredTrialTContCont: Plots the expected treatment effect on the true endpoint in a new trial (when both S and T are normally distributed continuous endpoints)

Description

The key motivation to evaluate a surrogate endpoint is to be able to predict the treatment effect on the true endpoint \(T\) based on the treatment effect on \(S\) in a new trial \(i=0\). The function Pred.TrialT.ContCont allows for making such predictions. The present plot function shows the results graphically.

Usage

# S3 method for PredTrialTContCont
plot(x, Size.New.Trial=5, CI.Segment=1, ...)

Arguments

x

A fitted object of class Pred.TrialT.ContCont, for details see Pred.TrialT.ContCont.

Size.New.Trial

The expected treatment effect on \(T\) is drawn as a black circle with size specified by Size.New.Trial. Default Size.New.Trial=5.

CI.Segment

The confidence interval around the expected treatment effect on \(T\) is depicted by a dashed horizontal line. By default, the width of the horizontal line of the horizontal section of the confidence interval indicator is \(2\) times the values specified by CI.Segment. Default \(CI.Segment=1\).

...

Extra graphical parameters to be passed to plot().

Author

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

See Also

Pred.TrialT.ContCont

Examples

if (FALSE)  # time consuming code part
# Generate dataset
Sim.Data.MTS(N.Total=2000, N.Trial=15, R.Trial.Target=.95, 
R.Indiv.Target=.8, D.aa=10, D.bb=50, 
Fixed.Effects=c(1, 2, 30, 90), Seed=1)

# Evaluate surrogacy using a reduced bivariate mixed-effects model
BimixedFit <- BimixedContCont(Dataset = Data.Observed.MTS, 
Surr = Surr, True = True, Treat = Treat, Trial.ID = Trial.ID, 
Pat.ID = Pat.ID, Model="Reduced")

# Suppose that in a new trial, it was estimated alpha_0 = 30
# predict beta_0 in this trial
Pred_Beta <- Pred.TrialT.ContCont(Object = BimixedFit, 
alpha_0 = 30)

# Examine the results
summary(Pred_Beta)

# Plot the results
plot(Pred_Beta)