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().

Author

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

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.

Examples

Run this code

# 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)