Produces a plot that provides a graphical representation of trial-level surrogacy based on the output of the `TrialLevelIT()`

function (information-theoretic framework).

1 2 3 4 |

`x` |
An object of class |

`Xlab.Trial` |
The legend of the X-axis of the plot that depicts trial-level surrogacy. Default "Treatment effect on the surrogate endpoint ( |

`Ylab.Trial` |
The legend of the Y-axis of the plot that depicts trial-level surrogacy. Default "Treatment effect on the true endpoint ( |

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

`Par` |
Graphical parameters for the plot. Default |

`...` |
Extra graphical parameters to be passed to |

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

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.

UnifixedContCont, BifixedContCont, UnifixedContCont, BimixedContCont, TrialLevelIT

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
# 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)
# Apply the function to estimate R^2_{h.t}
Fit <- TrialLevelIT(Alpha.Vector=Alpha.Vector,
Beta.Vector=Beta.Vector, N.Trial=50, Model="Reduced")
# Plot the results
plot(Fit)
``` |

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