plot.comb27.BinBin: Plots the distribution of prediction error functions in...

In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

Plots the distribution of prediction error functions in decreasing order of appearance.

Description

The function plot.comb27.BinBin plots each of the selected prediction functions in decreasing order in the single-trial causal-inference framework when both the surrogate and the true endpoints are binary outcomes. The distribution of frequencies at which each of the 27 possible predicton functions are selected provides additional insights regarding the association between S (\Delta_S) and T (\Delta_T).. See Details below.

Usage

## S3 method for class 'comb27.BinBin'
plot(x,lab,...)

Arguments

x	An object of class comb27.BinBin. See comb27.BinBin.
lab	a supplementary label to the graph.
...	Other arguments to be passed

Details

Each of the 27 prediction functions is coded as x/y/z with x, y and z taking values in {-1,0,1}. As an example, the combination 0/0/0 represents the prediction function that projects every value of \Delta_S to 0. Similarly, the combination -1/0/1 is the identity function projecting every value of \Delta_S to the same value for \Delta_T.

Value

An object of class comb27.BinBin with components,

index	count variable
Monotonicity	The vector of Monotonicity assumptions
Pe	The vector of the prediction error values.
combo	The vector containing the codes for the each of the 27 prediction functions.
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.

Author(s)

Paul Meyvisch, Wim Van der Elst, Ariel Alonso

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.

Alonso A, Van der Elst W and Meyvisch P (2016). Assessing a surrogate predictive value: A causal inference approach.

See Also

comb27.BinBin

Examples

## Not run:  # time consuming code part
CIGTS_27 <- comb27.BinBin(pi1_1_ = 0.3412, pi1_0_ = 0.2539, pi0_1_ = 0.119, 
                       pi_1_1 = 0.6863, pi_1_0 = 0.0882, pi_0_1 = 0.0784,  
                       Seed=1,Monotonicity=c("No"), M=500000) 
plot.comb27.BinBin(CIGTS_27,lab="CIGTS")

## End(Not run)

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