plot.MaxEntSPFBinBin: Plots the sensitivity-based and maximum entropy based...

In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

Plots the sensitivity-based and maximum entropy based surrogate predictive function (SPF) when S and T are binary outcomes.

Description

Plots the sensitivity-based (Alonso et al., 2015a) and maximum entropy based (Alonso et al., 2015b) surrogate predictive function (SPF), i.e., r(i,j)=P(\Delta T=i|\Delta S=j), in the setting where both S and T are binary endpoints. For example, r(-1,1) quantifies the probability that the treatment has a negative effect on the true endpoint (\Delta T=-1) given that it has a positive effect on the surrogate (\Delta S=1).

Usage

## S3 method for class 'MaxEntSPF.BinBin'
plot(x, SPF.Fit, Type="All.Histograms", Col="grey", ...)

Arguments

x	A fitted object of class MaxEntSPF.BinBin. See MaxEntSPFBinBin.
SPF.Fit	A fitted object of class SPF.BinBin. See SPF.BinBin.
Type	The type of plot that is requested. Possible choices are: Type="All.Histograms", the histograms of all 9 r(i,j)=P(\Delta T=i|\Delta S=j) vectors arranged in a 3 by 3 grid; Type="All.Densities", plots of densities of all r(i,j)=P(\Delta T=i|\Delta S=j) vectors. Default Type="All.Densities".
Col	The color of the bins or lines when histograms or density plots are requested. Default "grey".
...	Other arguments to be passed to the plot() function.

Author(s)

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

References

Alonso, A., Van der Elst, W., & Molenberghs, G. (2015a). Assessing a surrogate effect predictive value in a causal inference framework.

Alonso, A., & Van der Elst, W. (2015b). A maximum-entropy approach for the evluation of surrogate endpoints based on causal inference.

See Also

SPF.BinBin

Examples

# Sensitivity-based ICA results using ICA.BinBin.Grid.Sample
ICA <- ICA.BinBin.Grid.Sample(pi1_1_=0.341, pi0_1_=0.119, pi1_0_=0.254,
pi_1_1=0.686, pi_1_0=0.088, pi_0_1=0.078, Seed=1, 
Monotonicity=c("No"), M=5000)

# Sensitivity-based SPF
SPFSens <- SPF.BinBin(ICA)

# Maximum-entropy based SPF
SPFMaxEnt <- MaxEntSPFBinBin(pi1_1_=0.341, pi0_1_=0.119, pi1_0_=0.254,
pi_1_1=0.686, pi_1_0=0.088, pi_0_1=0.078)

# Plot results
plot(x=SPFMaxEnt, SPF.Fit=SPFSens)

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