ARE_tte {CompAREdesign} | R Documentation |

The composite endpoint is assumed to be a time to event 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 calculates the ARE (Assymptotic Relative Efficiency) method for time to event endpoints. The method quantifies the differences in efficiency of using the composite or the relevant as primary endpoint to lead the trial and, moreover, provides a decision rule to choose the primary endpoint. If the ARE is larger than 1, the composite endpoint may be considered the best option as primary endpoint. Otherwise, the relevant endpoint is preferred.

ARE_tte( p0_e1, p0_e2, HR_e1, HR_e2, beta_e1 = 1, beta_e2 = 1, case, copula = "Frank", rho = 0.3, rho_type = "Spearman" )

`p0_e1` |
numeric parameter between 0 and 1, expected proportion of observed events for the endpoint E1 |

`p0_e2` |
numeric parameter between 0 and 1, expected proportion of observed events for the endpoint E2 |

`HR_e1` |
numeric parameter between 0 and 1, expected cause specific hazard Ratio the endpoint E1 |

`HR_e2` |
numeric parameter between 0 and 1, expected cause specific hazard Ratio the endpoint E2 |

`beta_e1` |
numeric positive parameter, shape parameter ( |

`beta_e2` |
numeric positive parameter, shape parameter ( |

`case` |
integer parameter in 1,2,3,4 1: none of the endpoints is death 2: endpoint 2 is death 3: endpoint 1 is death 4: both endpoints are death by different causes |

`copula` |
character indicating the copula to be used: "Frank" (default), "Gumbel" or "Clayton". See details for more info. |

`rho` |
numeric parameter between -1 and 1, Spearman's correlation coefficient o Kendall Tau between the marginal distribution of the times to the two events E1 and E2. See details for more info. |

`rho_type` |
character indicating the type of correlation to be used: "Spearman" (default) or "Tau". See details for more info. |

Some parameters might be difficult to anticipate, especially the shape parameters of Weibull distributions and those referred to the relationship between the marginal distributions.
For the shape parameters (beta_e1, beta_e2) of the Weibull distribution, we recommend to use *β_j=0.5*, *β_j=1* or *β_j=2* if a decreasing, constant or increasing rates over time are expected, respectively.
For the correlation (rho) between both endpoints, generally a positive value is expected as it has no sense to design an study with two endpoints negatively correlated. We recommend to use *ρ=0.1*, *ρ=0.3* or *ρ=0.5* for weak, mild and moderate correlations, respectively.
For the type of correlation (rho_type), although two different type of correlations are implemented, we recommend the use of the Spearman's correlation.
In any case, if no information is available on these parameters, we recommend to use the default values provided by the function.

Returns the ARE value. If the ARE value is larger than 1 then the composite endpoint is preferred over the relevant endpoint. Otherwise, the endpoint 1 is preferred as the primary endpoint of the study.

Gomez Melis, G. and Lagakos, S.W. (2013). Statistical considerations when using a composite endpoint for comparing treatment groups. Statistics in Medicine. Vol 32(5), pp. 719-738. https://doi.org/10.1002/sim.5547

[Package *CompAREdesign* version 1.9 Index]