plot TrialLevelMA {Surrogate}

Provides a plots of trial-level surrogacy in the meta-analytic framework based on the output of the TrialLevelMA() function

Description

Produces a plot that provides a graphical representation of trial-level surrogacy based on the output of the TrialLevel() function (meta-analytic framework).

Usage

## S3 method for class 'TrialLevelMA'
plot(x, Weighted=TRUE, Xlab.Trial, 
Ylab.Trial, Main.Trial, Par=par(oma=c(0, 0, 0, 0), 
mar=c(5.1, 4.1, 4.1, 2.1)), ...)

Arguments

x

An object of class TrialLevelMA.

Weighted

Logical. If Weighted=TRUE, the circles that depict the trial-specific treatment effects on the true endpoint against the surrogate endpoint are proportional to the number of patients in the trial. If Weighted=FALSE, all circles have the same size. Default TRUE.

Xlab.Trial

The legend of the X-axis of the plot that depicts trial-level surrogacy. Default "Treatment effect on the surrogate endpoint (\alpha_{i})".

Ylab.Trial

The legend of the Y-axis of the plot that depicts trial-level surrogacy. Default "Treatment effect on the true endpoint (\beta_{i})".

Main.Trial

The title of the plot that depicts trial-level surrogacy. Default "Trial-level surrogacy".

Par

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

...

Extra graphical parameters to be passed to plot().

Author(s)

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

References

Buyse, M., Molenberghs, G., Burzykowski, T., Renard, D., & Geys, H. (2000). The validation of surrogate endpoints in meta-analysis of randomized experiments. Biostatistics, 1, 49-67.

See Also

UnifixedContCont, BifixedContCont, UnifixedContCont, BimixedContCont, TrialLevelMA

Examples

# Generate vector treatment effects on S
set.seed(seed = 1)
Alpha.Vector <- seq(from = 5, to = 10, by=.1) + runif(min = -.5, max = .5, n = 51)
# Generate vector treatment effects on T
set.seed(seed=2)
Beta.Vector <- (Alpha.Vector * 3) + runif(min = -5, max = 5, n = 51)
# Vector of sample sizes of the trials (here, all n_i=10)
N.Vector <- rep(10, times=51)

# Apply the function to estimate R^2_{trial}
Fit <- TrialLevelMA(Alpha.Vector=Alpha.Vector,
Beta.Vector=Beta.Vector, N.Vector=N.Vector)

# Plot the results and obtain summary
plot(Fit)
summary(Fit)