plot.jointSurroPenalloocv: Plot of trials leave-one-out crossvalidation Outputs from the one-step Joint surrogate model for evaluating a canditate surrogate endpoint.

Description

Plot of trials leave-one-out crossvalidation Outputs for evaluating the joint surrogate model

Usage

# S3 method for jointSurroPenalloocv
plot(x, unusedtrial = NULL, xleg = "bottomleft", 
yleg = NULL, main = NULL, xlab = "Trials", 
ylab = "Log Hazard ratio of the true endpoint", 
legend = c("Beta observed", "Beta predict"), ...)

Arguments

An object inherent from the jointSurroPenalloocv Class

unusedtrial

Vector of unconsidered trials, may be due to the fact that the predicted treatment effects on true endpoint have an outlier. In this case, one can drop from the data the trials with very hight absolute predicted value

xleg

X-coordinate for the location of the legend.

yleg

Y-coordinate for the location of the legend, the default is NULL

main

An overall title for the plot: see title.

xlab

A title for the x axis: see title.

ylab

A title for the y axis: see title.

legend

A vector of characters string of length >= 1 to appear in the legend

...

other unused arguments.

Value

This function displays the boxplots corresponding to the number of trials in the dataset. Each boxplot includes 3 elements corresponding to the predicted treatment effect on true endpoint with the prediction interval. The circles inside or outside the boxplot represent the observed treatment effects on true endpoint. For each trial with convergence issues or outliers, the boxplot is replaced by a dash. In this case, we display in the title of the figure a vector of these trials, if argument main is set to NULL. The function returns the list of unused trials.

References

Burzykowski T, Buyse M (2006). "Surrogate threshold effect: an alternative measure for meta-analytic surrogate endpoint validation." Pharmaceutical Statistics, 5(3), 173-186.ISSN 1539-1612.

Examples

Run this code

# NOT RUN {

# }
# NOT RUN {
# Generation of data to use 
 data.sim <- jointSurrSimul(n.obs=300, n.trial = 10,cens.adm=549.24,
             alpha = 1.5, theta = 3.5, gamma = 2.5, zeta = 1, sigma.s = 0.7,
             sigma.t = 0.7, cor = 0.8, betas = -1.25, betat = -1.25,
             full.data = 0, random.generator = 1, seed = 0,
             nb.reject.data = 0)

###--- Joint surrogate model ---###
 
joint.surro.sim.MCGH <- jointSurroPenal(data = data.sim, int.method = 2,
                        nb.mc = 300, nb.gh = 20, print.iter = T)
        
# Example of loocv taking into accountn ony trial 2 trials (1 and 3)
dloocv <- loocv(joint.surro.sim.MCGH, unusedtrial = c(2,4:10))

plot(x = dloocv, xleg = "topright", bty = "n")

# }
# NOT RUN {
# }