Surrogate (version 1.7)

plot Causal-Inference ContCont: Plots the (Meta-Analytic) Individual Causal Association when S and T are continuous outcomes

Description

This function provides a plot that displays the frequencies, percentages, or cumulative percentages of the individual causal association (ICA; \(\rho_{\Delta}\)) and/or the meta-analytic individual causal association (MICA; \(\rho_{M}\)) values. These figures are useful to examine the sensitivity of the obtained results with respect to the assumptions regarding the correlations between the counterfactuals (for details, see Alonso et al., submitted; Van der Elst et al., submitted). Optionally, it is also possible to obtain plots that are useful in the examination of the plausibility of finding a good surrogate endpoint when an object of class ICA.ContCont is considered.

Usage

# S3 method for ICA.ContCont
plot(x, Xlab.ICA, Main.ICA, Type="Percent", 
Labels=FALSE, ICA=TRUE, Good.Surr=FALSE, Main.Good.Surr, 
Par=par(oma=c(0, 0, 0, 0), mar=c(5.1, 4.1, 4.1, 2.1)), col, …)
# S3 method for MICA.ContCont
plot(x, ICA=TRUE, MICA=TRUE, Type="Percent", 
Labels=FALSE, Xlab.ICA, Main.ICA, Xlab.MICA, Main.MICA,
Par=par(oma=c(0, 0, 0, 0), mar=c(5.1, 4.1, 4.1, 2.1)), col, …)

Arguments

x

An object of class ICA.ContCont or MICA.ContCont. See ICA.ContCont or MICA.ContCont.

ICA

Logical. When ICA=TRUE, a plot of the ICA is provided. Default TRUE.

MICA

Logical. This argument only has effect when the plot() function is applied to an object of class MICA.ContCont. When MICA=TRUE, a plot of the MICA is provided. Default TRUE.

Type

The type of plot that is produced. When Type=Freq or Type=Percent, the Y-axis shows frequencies or percentages of \(\rho_{\Delta}\), \(\rho_{M}\), and/or \(\delta\). When Type=CumPerc, the Y-axis shows cumulative percentages of \(\rho_{\Delta}\), \(\rho_{M}\), and/or \(\delta\). Default "Percent".

Labels

Logical. When Labels=TRUE, the percentage of \(\rho_{\Delta}\), \(\rho_{M}\), and/or \(\delta\) values that are equal to or larger than the midpoint value of each of the bins are displayed (on top of each bin). Default FALSE.

Xlab.ICA

The legend of the X-axis of the ICA plot. Default "\(\rho_{\Delta}\)".

Main.ICA

The title of the ICA plot. Default "ICA".

Xlab.MICA

The legend of the X-axis of the MICA plot. Default "\(\rho_{M}\)".

Main.MICA

The title of the MICA plot. Default "MICA".

Good.Surr

Logical. When Good.Surr=TRUE, a plot of \(\delta\) is provided. This plot is useful in the context of examinating the plausibility of finding a good surrogate endpoint. Only applies when an object of class ICA.ContCont is considered. For details, see Alonso et al. (submitted). Default FALSE.

Main.Good.Surr

The title of the plot of \(\delta\). Only applies when an object of class ICA.ContCont is considered. For details, see Alonso et al. (submitted).

Par

Graphical parameters for the plot. Default par(oma=c(0, 0, 0, 0), mar=c(5.1, 4.1, 4.1, 2.1)).

col

The color of the bins. Default col <- c(8).

…

Extra graphical parameters to be passed to hist().

References

Alonso, A., Van der Elst, W., Molenberghs, G., Buyse, M., & Burzykowski, T. (submitted). On the relationship between the causal inference and meta-analytic paradigms for the validation of surrogate markers.

Van der Elst, W., Alonso, A., & Molenberghs, G. (submitted). An exploration of the relationship between causal inference and meta-analytic measures of surrogacy.

See Also

ICA.ContCont, MICA.ContCont, plot MinSurrContCont

Examples

# NOT RUN {
# Plot of ICA

# Generate the vector of ICA values when rho_T0S0=rho_T1S1=.95, and when the
# grid of values {0, .2, ..., 1} is considered for the correlations
# between the counterfactuals:
SurICA <- ICA.ContCont(T0S0=.95, T1S1=.95, T0T1=seq(0, 1, by=.2), T0S1=seq(0, 1, by=.2), 
T1S0=seq(0, 1, by=.2), S0S1=seq(0, 1, by=.2))

# Plot the results:
plot(SurICA)

# Same plot but add the percentages of ICA values that are equal to or larger 
# than the midpoint values of the bins
plot(SurICA, Labels=TRUE)

# Plot of both ICA and MICA

# Generate the vector of ICA and MICA values when R_trial=.8, rho_T0S0=rho_T1S1=.8, 
# D.aa=5, D.bb=10, and when the grid of values {0, .2, ..., 1} is considered 
# for the correlations between the counterfactuals:
SurMICA <- MICA.ContCont(Trial.R=.80, D.aa=5, D.bb=10, T0S0=.8, T1S1=.8, 
T0T1=seq(0, 1, by=.2), T0S1=seq(0, 1, by=.2), T1S0=seq(0, 1, by=.2), 
S0S1=seq(0, 1, by=.2))

# Plot the vector of generated ICA and MICA values
plot(SurMICA, ICA=TRUE, MICA=TRUE)
# }