Description Usage Arguments Value Author(s) References See Also Examples

calculates sample size for group sequential designs in equivalence studies that can stop for equivalence, or for either equivalence or futility (binding or non-binding). The calculated samples size is referred to as n.minmax, "min" in the sense that the calculcated n is the minimunm required sample size to reach a given power level, "max" in the sense that it would the max spent sample size which only happens if the study stop in the last stage

1 2 3 |

`l` |
lower equivalence bound as given in the equivalence hypothesis |

`u` |
upper equivalence bound as given in the equivalence hypothesis |

`theta` |
true mean difference between 2 groups |

`sigma` |
between-subject standard deviation of the response variable for two independent groups; within subject standard deviation of the response variable for paired groups |

`n1.lower` |
the lower bound of the interval from which n.minmax in group 1 will be solved using a bisection method |

`n2.lower` |
the lower bound of the interval from which n.minmax in group 2 will be solved using a bisection method |

`t.vec` |
cumulative time points for the interim looks assuming a constant accrual rate. For example, if a study has equally spaced 4 looks including the final look, then t.vec=1:4/4. It can any vector as long as it is increasing and the last element is 1. |

`type1` |
overall Type I error rate |

`type2` |
overall Type II error rate |

`gamma` |
The gamma parameter in the gamma cumulative error spending function (Hwang, Shih, and DeCani 1990). Error spending given a t.vec is = total error rate*(1-exp(-gamma*t.vec))/(1-exp(-gamma)). gamma= 1 produces Pocock-type error spending function; gamma = -4 produces O'Brien-Fleming type error spending function. Default gamma = -4 |

`binding` |
whether the futility boundaries are binding; default = FALSE |

`n1.upper` |
the upper bound of the interval from which n.minmax in group 1 will be solved using a bisection method; default = 2*n1.lower |

`n2.upper` |
the upper bound of the interval from which n.minmax in group 2 will be solved using a bisection method; default = 2*n2.lower |

`n.sim` |
number of randomly simulated samples in computation of n.minmax via the Monte Carlo simulation approach. Default n.sim=1e4 |

`seed` |
seed used in the Monte Carlo computation. If non-specified, the seed is set randomly. |

`n1minmax` |
n.minmax in group 1 |

`n2minmax` |
n.minmax in group 2 |

`typeI` |
vector of cumulative stage Type I error rate |

`typeII` |
vector of cumulative stage Type II error rate |

`equivL` |
vector of the equivalence boundary c(L) at each stage |

`equivU` |
vector of the equivalence boundary c(U) at each stage |

`futilL` |
vector of the futility boundary d(L) at each stage |

`futilU` |
vector of the futility boundary d(U) at each stage |

Fang Liu ([email protected])

Liu, F. and Li, Q. (2014), Sequential Equivalence Testing based on the Exact Distribution of Bivariate Noncentral $t$-statistics, Computational Statistics and Data Analysis, 77:14-24

Liu, F. (2014), gset: an R package for exact sequential test of equivalence hypothesis based on bivariate non-central $t$-statistics, the R Journal (to appear)

`nonbinding`

,`binding`

,`equivonly`

, `nfix`

, `oc`

,`figureE`

,`figureEF`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
## Not run:
L <- -0.2
U <- 0.2
theta <- 0
sigma <- 0.4
alpha <- 0.05
beta <- 0.2
K <- 4
r <- 1
# the sample size per group with a traditional nonsequential design
n.fix <- nfix(r, L,U,theta,sigma,alpha,beta)
# nminmax with nonbinding futility
bound1 <- nminmax(L, U, theta, sigma, n.fix$n1, n.fix$n2, 1:K/K, alpha, beta)
figureEF(bound1, K)
# nminmax with binding futility
bound2 <- nminmax(L, U, theta, sigma, n.fix$n1, n.fix$n2, 1:K/K, alpha, beta, binding=TRUE)
figureEF(bound2, K)
## End(Not run)
``` |

gset documentation built on May 30, 2017, 5:12 a.m.

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