n.opt {bdpopt} | R Documentation |

Find an approximation of the optimal sample size and corresponding expected utility for a simple phase III clinical trial model with a single, normally distributed response and a utility function of a fixed form.

n.opt(nu = 0, tau = 1, sigma = 1, alpha = 0.025, gain.constant = 1, gain.function = function(X, mu) 0, fixed.cost = 0, sample.cost = 0.005, k = 1, n.min = 1, n.max = 50, n.step = 1, n.iter = 10000, n.burn.in = 1000, n.adapt = 1000, regression.type = "loess", plot.results = TRUE, independent.SE = FALSE, parallel = FALSE, path.to.package = NA)

`nu` |
The mean of the conjugate normal prior distribution for the unknown population mean. |

`tau` |
The standard deviation of the conjugate normal prior distribution for the unknown population mean. |

`sigma` |
The known population standard deviation for each individual response in the trial. |

`alpha` |
The significance level in the one-sided test used by the regulatory authority to decide upon marketing approval for the new treatment. |

`gain.constant` |
A constant utility gain received upon treatment approval. The total
gain consists of the sum of |

`gain.function` |
A variable utility gain obtained in addition to the constant utility gain upon treatment approval. |

`fixed.cost` |
The fixed cost of performing the trial. |

`sample.cost` |
The marginal cost per observation for the trial. |

`k` |
The number independent, parallel trials. Must be an integer greater than zero. |

`n.min` |
Lower limit for the one-dimensional grid for the sample size. |

`n.max` |
Upper limit for the one-dimensional grid for the sample size. |

`n.step` |
The step size of the grid for the sample size. |

`n.iter` |
The number of iterations in the JAGS MCMC simulation. |

`n.burn.in` |
The number of burn iterations prior to the JAGS MCMC simulation. |

`n.adapt` |
The number of adaptation iterations prior to the burn in and JAGS MCMC simulation. |

`regression.type` |
If set to |

`plot.results` |
Set to |

`independent.SE` |
If |

`parallel` |
Set to |

`path.to.package` |
The search path to the installation directory of bdpopt. For the
default value, the function will attempt to find the path using |

A list with components

`ns` |
A numeric, atomic vector containing the sample size grid points. |

`eus` |
A numeric, atomic vector containing the sample means of the simulated expected utilities corresponding to the sample size grid points. |

`opt.arg` |
The optimal sample size found by maximising the estimated expected utility. |

`opt.eu` |
The estimated optimal utility corresponding to the optimal sample size found. |

Sebastian Jobjörnsson jobjorns@chalmers.se

[Package *bdpopt* version 1.0-1 Index]