plot.CausalInference: Plots the (Meta-Analytic) Individual Causal Association when...
In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

plot Causal-Inference ContCont

R Documentation

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 class '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 class '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(s)

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

# 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)

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