Description Usage Arguments Details Value References See Also

View source: R/feasibility_check.R

Takes as input a list of arguments (args) that define an adaptive trial (see `buildTrial`

or `optimizeTrial`

). This function adjusts the `n_total`

argument in order to find the smallest maximum sample size that meets the power constraints specified in the `cases`

argument.

1 2 | ```
min_n_multistage(args, cases, trial_method, objective_fun, min_n = 1,
max_n = min_n * 1000, step_n = 10, showiter = FALSE)
``` |

`args` |
a list containing a subset of the arguments for the functions |

`cases` |
A list of power constraints, in the same format as those sent to |

`trial_method` |
either 'cov' or 'MB' for Maurer-Bretz (2013). |

`objective_fun` |
see |

`min_n` |
The smallest sample size to consider |

`max_n` |
The largest sample size to consider |

`step_n` |
The step size to consider when carrying out the binary search. For example, if |

`showiter` |
passed to |

This function requires that the objective function contain a 'base' element, and a 'power_diffs' element that is nonnegative when power constraints are met. For example, see `min_E_SS_power_constraints`

.

A list containing

`n` |
The smallest feasible n_total |

`soln` |
Output from |

.

Maurer, W. and F. Bretz (2013). Multiple testing in group sequential trials using graphical approaches. *Statistics in Biopharmaceutical Research.*

`min_n_feasible`

, `feasibility_check`

aaronjfisher/designOptim documentation built on April 3, 2018, 8:25 p.m.

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