# 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) 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). ```r ## S3 method for class '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 class '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 class '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()`. ## References Alonso, A, & Molenberghs, G. (2007). Surrogate marker evaluation from an information theory perspective. **Biometrics, 63,** 180-186. ## Author(s) Wim Van der Elst, Ariel Alonso, & Geert Molenberghs ## See Also FixedBinBinIT , FixedBinContIT , FixedContBinIT ## Examples ```r ## Not run: # 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) ## End(Not run) ```

plot.InformationTheoreticBinCombn function