Description Usage Arguments Details Value Author(s) References Examples

Data are generated from a mixed proportional hazard model, a Clayton copula model (Burzykowski and Cortinas Abrahantes, 2005), a Gumbel-Hougaard copula model, or a mixture of half-normal and exponential random variables (Shi et al., 2011).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ```
simData.re(R2 = 0.6, N = 30, ni = 200,
nifix = TRUE, gammaWei = c(1, 1), censorT, censorA,
kTau= 0.6, baseCorr = 0.5, baseVars = c(0.2, 0.2),
alpha = 0, beta = 0,
alphaVar = 0.1, betaVar = 0.1,
mstS = 4 * 365.25, mstT = 8 * 365.25)
simData.cc(R2 = 0.6, N = 30, ni = 200,
nifix = TRUE, gammaWei = c(1, 1), censorT, censorA,
kTau= 0.6, baseCorr = 0.5, baseVars = c(0.2, 0.2),
alpha = 0, beta = 0,
alphaVar = 0.1, betaVar = 0.1,
mstS = 4 * 365.25, mstT = 8 * 365.25)
simData.gh(R2 = 0.6, N = 30, ni = 200,
nifix = TRUE, gammaWei = c(1, 1), censorT, censorA,
kTau= 0.6, baseCorr = 0.5, baseVars = c(0.2, 0.2),
alpha = 0, beta = 0,
alphaVar = 0.1, betaVar = 0.1,
mstS = 4 * 365.25, mstT = 8 * 365.25)
simData.mx(R2 = 0.6, N = 30, ni = 200,
nifix = TRUE, gammaWei = c(1, 1), censorT, censorA,
indCorr = TRUE, baseCorr = 0.5, baseVars = c(0.2, 0.2),
alpha = 0, beta = 0,
alphaVar = 0.1, betaVar = 0.1,
mstS = 4 * 365.25, mstT = 8 * 365.25)
``` |

`R2` |
The desired trial-level surrogacy |

`N` |
The number of trials |

`ni` |
The (fixed or average) number of patients per trial |

`nifix` |
Should all trials have the same size (if |

`gammaWei` |
The shape parameter(s) of the Weibull distributions. Either one or two values. If one value is provided, it is used for both endpoints |

`censorT` |
censoring rate for the true endpoint T (before adding administrative censoring) |

`censorA` |
administrative censoring at time censorA |

`kTau` |
The desired individual-level dependence between S and T (Kendall's tau) |

`indCorr` |
Should S and T be correlated or not? (for |

`baseCorr` |
correlation between baseline hazards ( |

`baseVars` |
variances of baseline random effects (S and T) |

`alpha` |
average treatment effect on S |

`beta` |
average treatment effect on T |

`alphaVar` |
variance of |

`betaVar` |
variance of |

`mstS` |
median survival time for S in the control arm |

`mstT` |
median survival time for T in the control arm |

The function `simData.re`

generates data from a proportional hazard model
with random effects at individual level and
random effects and random treatment effects at trial level.
Individual dependence can be tuned in terms of Kendall's *tau*
(`kTau`

).

The function `simData.cc`

generates data from a Copula function
as shown by Burzykowski and Cortinas Abrahantes (2005).
Individual dependence can be tuned in terms of Kendall's *tau*
(`kTau`

).

The function `simData.mx`

implements the simulation method by Shi et al. (2011).
This model is based on a mixture of half-normal and exponential random variables.
Under this model, individual dependence can be induced by using the same
half-normal random variable for S and T.
This is obtained by setting `indCorr = TRUE`

,
but the amount of correlation is not dependent on a single parameter.

A data.frame with columns

`trialref` |
the trial reference |

`trt` |
the treatment arm (-0.5 or 0.5) |

`id` |
the patient id |

`timeT` |
the value of the true endpoint T |

`statusT` |
the censoring/event (0/1) indicator of the true endpoint T |

`timeS` |
the value of the surrogate endpoint S |

`statusS` |
the censoring/event (0/1) indicator of the surrogate endpoint S |

Federico Rotolo [aut], Xavier Paoletti [ctr], Marc Buyse [ctr], Tomasz Burzykowski [ctr], Stefan Michiels [ctr, cre]

Burzykowski T, Cortinas Abrahantes J (2005). Validation in the case of two failure-time endpoints.
In *The Evaluation of Surrogate Endpoints* (pp. 163-194). Springer, New York.

Rotolo F, Paoletti X, Burzykowski T, Buyse M, Michiels S.
A Poisson approach for the validation of failure time surrogate endpoints
in individual patient data meta-analyses.
*Statistical Methhods in Medical Research* 2019;
**28(1)**.
doi: 10.1177/0962280217718582

Shi Q, Renfro LA, Bot BM, Burzykowski T, Buyse M, Sargent DJ.
Comparative assessment of trial-level surrogacy measures for candidate
time-to-event surrogate endpoints in clinical trials.
*Computational Statistics & Data Analysis* 2011;
**55**: 2748–2757.

1 2 3 4 | ```
set.seed(1)
simData.re(N = 2, ni = 5)
simData.cc(N = 2, ni = 5)
simData.mx(N = 2, ni = 5)
``` |

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