Description Usage Arguments References See Also Examples

getSolutions uses a "simon"-object to calculate two-stage designs as they were described by Simon.

1 2 | ```
getSolutions(simon = setupSimon(), useCurtailment = FALSE,
curtail_All = FALSE, cut = 0, replications = 10000, upperBorder = 0)
``` |

`simon` |
a "simon"-object which will be used to calculate designs. |

`useCurtailment` |
boolean value determining whether (non-)stochastic curtailment is used. |

`curtail_All` |
boolean value; if true the effect of (non-)stochastic curtailment will be calculated for different cut points in 0.05 steps starting with the value of the parameter "cut". |

`cut` |
sets the "cut point" used to calculate the effect of (non-)stochastic curtailment. A study is stopped if the conditional power falls below the value of "cut". |

`replications` |
number of simulations to estimate the effect of (non-)stochastic curtailment. |

`upperBorder` |
maximal possible value for n. If set to sero (default) the programm will aproximate a upper border automaticly. |

Simon, R. (1989): Optimal two-stage designs for phase II clinical trials. Controlled Clinical Trials 10,1-10.

Kunz C.U., Kieser M (2012): Curtailment in single-arm two-stage phase II oncology trials. Biometrical Journal 54, 445-456

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
# Example 1: Using the default values
designs <- getSolutions()
designs <- designs$Solutions
designs
# Example 2: Setting up a "simon"-object, then calculate designs
simon <- setupSimon(alpha = 0.1, beta = 0.2, p0 = 0.3, p1 = 0.5)
designs <- getSolutions(simon)$Solutions
designs
# Esample 3: Calculating designs and simulating the influence of
# stochastic curtailment for each design.
simon <- setupSimon(alpha = 0.1, beta = 0.2, p0 = 0.3, p1 = 0.5)
designs <- getSolutions(simon, useCurtailment = TRUE, curtail_All = TRUE, cut = 0.3)
#List containing the found designs, the influence of stochastic curtailment
# and the regarding stopping rules.
designs
``` |

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