mv.plots.SM | R Documentation |

Plots mean value of upper limit, lower limit and interval width for four different ranking methods. This function is basically a wrapper for mv.plot.

mv.plots.SM(n, a, b, type = "interval", B = 100, offset = TRUE, plt = c(1, 1, 1), p0 = NULL, p1 = NULL, focus = FALSE)

`n` |
Design vector of planned sample sizes |

`a` |
Design vector of lower futility boundaries |

`b` |
Design vector of upper superiority boundaries |

`type` |
Either "upper", "lower" or "interval" (default) |

`B` |
Integer controlling fineness of plot (default=100) |

`offset` |
if TRUE then ML mean value is subtracted |

`plt` |
Logical vector indicating output plots of upper, lower and interval (default=c(1,1,1)) |

`p0` |
Lower (null) benchmark for success probability |

`p1` |
Upper (alternative) benchmark for success probability |

`focus` |
Logical. If true, plots are restricted to p between p0 and p1. (default=FALSE) |

NULL

Chris J. Lloyd

Lloyd, C.J. (2021) Exact confidence limits after a group sequential single arm binary trial. Statistics in Medicine, Volume 38, 2389-2399. doi: 10.1002/sim.8909

# Figure 2 in Lloyd (2020) n=c(5,6,5,9) a=c(2,4,5,12) b=c(5,9,11,13) p0=.4 p1=.75 mv.plots.SM(n,a,b,p0=p0,p1=p1) # Produces three panel graphic identical to Figure 2 in reference mv.plots.SM(n,a,b,p0=p0,p1=p1,focus=TRUE) # Produces alternative graphic focussed on relevant values of p. # In both cases LR (in blue) appears best. CP can perform poorly # for values of p outside the range of interest.

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