plot.PPE.BinBin: Plots the distribution of either PPE, RPE or R^2_{H} either...

In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

Plots the distribution of either PPE, RPE or R^2_{H} either as a density or as a histogram in the setting where both S and T are binary endpoints

Description

The function plot.PPE.BinBin plots the distribution of PPE, RPE or R^2_{H} in the setting where both surrogate and true endpoints are binary in the single-trial causal-inference framework. See Details below.

Usage

## S3 method for class 'PPE.BinBin'
plot(x,Type="Density",Param="PPE",Xlab.PE,main.PE,
ylab="density",Cex.Legend=1,Cex.Position="bottomright", lwd=3,linety=1,color=1,
Breaks=0.05, xlimits=c(0,1), ...)

Arguments

x	An object of class PPE.BinBin. See PPE.BinBin.
Type	The type of plot that is produced. When Type="Freq", a histogram is produced. When Type="Density", a density is produced. Default Type="Density".
Param	Parameter to be plotted: is either "PPE", "RPE" or "ICA"
Xlab.PE	The label of the X-axis when density plots or histograms are produced.
main.PE	Title of the density plot or histogram.
ylab	The label of the Y-axis for the density plots. Default ylab="density".
Cex.Legend	The size of the legend. Default Cex.Legend=1.
Cex.Position	The position of the legend. Default Cex.Position="bottomright".
lwd	The line width for the density plot. Default lwd=3.
linety	The line types for the density. Default linety=1.
color	The color of the density or histogram. Default color=1.
Breaks	The breaks for the histogram. Default Breaks=0.05.
xlimits	The limits for the X-axis. Default xlimits=c(0,1).
...	Other arguments to be passed.

Details

In the continuous normal setting, surroagacy can be assessed by studying the association between the individual causal effects on S and T (see ICA.ContCont). In that setting, the Pearson correlation is the obvious measure of association.

When S and T are binary endpoints, multiple alternatives exist. Alonso et al. (2016) proposed the individual causal association (ICA; R_{H}^{2}), which captures the association between the individual causal effects of the treatment on S (\Delta_S) and T (\Delta_T) using information-theoretic principles.

The function PPE.BinBin computes R_{H}^{2} using a grid-based approach where all possible combinations of the specified grids for the parameters that are allowed that are allowed to vary freely are considered. It additionally computes the minimal probability of a prediction error (PPE) and the reduction on the PPE using information that S conveys on T. Both measures provide complementary information over the R_{H}^{2} and facilitate more straightforward clinical interpretation.

Value

An object of class PPE.BinBin with components,

index	count variable
PPE	The vector of the PPE values.
RPE	The vector of the RPE values.
PPE_T	The vector of the PPE_T values indicating the probability on a prediction error without using information on S.
R2_H	The vector of the R_H^2 values.
H_Delta_T	The vector of the entropies of \Delta_T.
H_Delta_S	The vector of the entropies of \Delta_S.
I_Delta_T_Delta_S	The vector of the mutual information of \Delta_S and \Delta_T.
Pi.Vectors	An object of class data.frame that contains the valid \pi vectors.

Author(s)

Paul Meyvisch, Wim Van der Elst, Ariel Alonso, Geert Molenberghs

References

Alonso A, Van der Elst W, Molenberghs G, Buyse M and Burzykowski T. (2016). An information-theoretic approach for the evaluation of surrogate endpoints based on causal inference.

Meyvisch P., Alonso A.,Van der Elst W, Molenberghs G. (2018). Assessing the predictive value of a binary surrogate for a binary true endpoint, based on the minimum probability of a prediction error.

See Also

PPE.BinBin

Examples

## Not run: # Time consuming part
PANSS <- PPE.BinBin(pi1_1_=0.4215, pi0_1_=0.0538, pi1_0_=0.0538,
                   pi_1_1=0.5088, pi_1_0=0.0307,pi_0_1=0.0482, 
                   Seed=1, M=2500) 
                   
plot(PANSS,Type="Freq",Param="RPE",color="grey",Breaks=0.05,xlimits=c(0,1),main="PANSS")

## End(Not run)

Surrogate documentation built on June 22, 2024, 9:16 a.m.