View source: R/eselectsim_ub.R

eselectsim_ub | R Documentation |

This function simulates trials with endpoint selection and sample size reassessment for composite binary endpoints based on unblinded data. The composite endpoint is assumed to be a binary endpoint formed by a combination of two events (E1 and E2). We assume that the endpoint 1 is more relevant for the clinical question than endpoint 2. This function simulates a trial based on the design parameters and use the algorithm implemented in eselect() to select the primary endpoint and recalculate the sample size accordingly.

eselectsim_ub( ss_arm, p0_e1, OR1, p0_e2, OR2, p0_ce, p_init = 1, criteria = "SS", H0_e1 = FALSE, H0_e2 = FALSE, SS_r = TRUE, alpha = 0.05, beta = 0.2 )

`ss_arm` |
numeric parameter, sample size per arm |

`p0_e1` |
numeric parameter, probability of occurrence E1 in the control group |

`OR1` |
numeric parameter, Odds ratio for the endpoint 1 |

`p0_e2` |
numeric parameter, probability of occurrence E2 in the control group |

`OR2` |
numeric parameter, Odds ratio for the endpoint 2 |

`p0_ce` |
numeric parameter, probability of composite endpoint in the control group |

`p_init` |
numeric parameter, percentage of sample size used in the interim |

`criteria` |
decision criteria to choose between the composite endpoint or the endpoint 1 as primary endpoint ("SS": Ratio sample sizes, "ARE": Asymptotic Relative Efficiency). |

`H0_e1` |
Simulate under true null hypothesis for the endpoint E1 (TRUE/FALSE). |

`H0_e2` |
Simulate under true null hypothesis for the endpoint E2 (TRUE/FALSE). |

`SS_r` |
Sample size reassessment (TRUE/FALSE). If TRUE, in those cases where the sample size is less than the needed for achieving the pre-specified power, additional subjects are added after recalculating the sample size. If FALSE, no more subjects are added in the study. |

`alpha` |
Type I error. |

`beta` |
Type II error. |

This function returns the decision (Decision = 1, meaning the chosen endpoint is the composite endpoint; and Decision = 0, meaning the chosen endpoint is the relevant endpoint) and the statistic to test the primary hypothesis according to the decision.

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