plot Information-Theoretic BinCombn: Provides plots of trial- and individual-level surrogacy in the Information-Theoretic framework when both S and T are binary, or when S is binary and T is continuous (or vice versa)

Description

Produces plots that provide a graphical representation of trial- and/or individual-level surrogacy (R2_ht and R2_hInd per cluster) based on the Information-Theoretic approach of Alonso & Molenberghs (2007).

Usage

# S3 method for FixedBinBinIT
plot(x, Trial.Level=TRUE, Weighted=TRUE, Indiv.Level.By.Trial=TRUE, 
Xlab.Indiv, Ylab.Indiv, Xlab.Trial, Ylab.Trial, Main.Trial, Main.Indiv, 
Par=par(oma=c(0, 0, 0, 0), mar=c(5.1, 4.1, 4.1, 2.1)), ...)
# S3 method for FixedBinContIT
plot(x, Trial.Level=TRUE, Weighted=TRUE, Indiv.Level.By.Trial=TRUE, 
Xlab.Indiv, Ylab.Indiv, Xlab.Trial, Ylab.Trial, Main.Trial, Main.Indiv, 
Par=par(oma=c(0, 0, 0, 0), mar=c(5.1, 4.1, 4.1, 2.1)), ...)
# S3 method for FixedContBinIT
plot(x, Trial.Level=TRUE, Weighted=TRUE, Indiv.Level.By.Trial=TRUE, 
Xlab.Indiv, Ylab.Indiv, Xlab.Trial, Ylab.Trial, Main.Trial, Main.Indiv, 
Par=par(oma=c(0, 0, 0, 0), mar=c(5.1, 4.1, 4.1, 2.1)), ...)

Arguments

x: An object of class FixedBinBinIT, FixedBinContIT, or FixedContBinIT.
Trial.Level: Logical. If Trial.Level=TRUE, a plot of the trial-specific treatment effects on the true endpoint against the trial-specific treatment effect on the surrogate endpoints is provided (as a graphical representation of \(R_{ht}\)). Default TRUE.
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.
Indiv.Level.By.Trial: Logical. If Indiv.Level.By.Trial=TRUE, a plot that shows the estimated \(R^2_{h.ind}\) for each trial (and confidence intervals) is provided. Default TRUE.
Xlab.Indiv: The legend of the X-axis of the plot that depicts the estimated \(R^2_{h.ind}\) per trial. Default "\(R[h.ind]^{2}\).
Ylab.Indiv: The legend of the Y-axis of the plot that shows the estimated \(R^2_{h.ind}\) per trial. Default "Trial".
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.Indiv: The title of the plot that depicts individual-level surrogacy. Default "Individual-level surrogacy".
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

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

References

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

Examples

Run this code

if (FALSE)  # Time consuming (>5sec) code part
# Generate data with continuous Surr and True
Sim.Data.MTS(N.Total=5000, N.Trial=50, R.Trial.Target=.9, R.Indiv.Target=.9,
             Fixed.Effects=c(0, 0, 0, 0), D.aa=10, D.bb=10, Seed=1,
             Model=c("Full"))
# Dichtomize Surr and True
Surr_Bin <- Data.Observed.MTS$Surr
Surr_Bin[Data.Observed.MTS$Surr>.5] <- 1
Surr_Bin[Data.Observed.MTS$Surr<=.5] <- 0
True_Bin <- Data.Observed.MTS$True
True_Bin[Data.Observed.MTS$True>.15] <- 1
True_Bin[Data.Observed.MTS$True<=.15] <- 0
Data.Observed.MTS$Surr <- Surr_Bin
Data.Observed.MTS$True <- True_Bin

# Assess surrogacy using info-theoretic framework
Fit <- FixedBinBinIT(Dataset = Data.Observed.MTS, Surr = Surr, 
True = True, Treat = Treat, Trial.ID = Trial.ID, 
Pat.ID = Pat.ID, Number.Bootstraps=100)

# Examine results
summary(Fit)
plot(Fit, Trial.Level = FALSE, Indiv.Level.By.Trial=TRUE)
plot(Fit, Trial.Level = TRUE, Indiv.Level.By.Trial=FALSE)