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

For the problem of comparing means of k treatment groups to the mean of one control group. The implementation of the function needs the following three assumptions: 1. The k treatment groups have identical treatment effect size. 2. The samples to be assigned to each of the k treatment groups are expected to be equal at size n. 3. The alternative hypotheses are one-sided. With the violations of either of the assumptions, simulation-based power evaluation is suggested.

1 |

`r` |
The least number of null hypotheses to be rejected, e.g.,when r=1, the disjunctive power is returned and when r=k, the conjunctive power is returned. |

`k` |
Number of hypotheses to be tested, |

`mu` |
Assumed population mean in each of the k treatment groups. |

`mu0` |
Assumed population mean in the control group. |

`n` |
Sample size in each of the k treatment groups |

`n0` |
Sample size in the control group |

`contrast` |
If mu and mu0 are concerned of mean of a continous outcome, specify contrast="means"; if mu and mu0 are concerned of proportion of binary outcome, specify contrast="props". |

`sigma` |
The population error variance, which should be specified when contrast="means"; if contrast="props", set sigma=NA as default and it will be calculated based on mu and mu0 specified within the function. |

`df` |
Degree of freedom of the t-test statistics. When (approximately) normally distributed test statistics are applied, set df=Inf (default). |

`alpha` |
The pre-specified overall significance level, default=0.05. |

`mcs` |
The number of monte-carlo sample points to numerically approximate the power for a given sample size, refer to Equation (4.3) and Equation (4.5) in Dunnett and Tamhane (1992). |

`testcall` |
The applied Dunnett test procedure: "SD"=step-down Dunnett test; "SU"=step-up Dunnett test. |

Return the power of rejecting at least r out of the k false null hypotheses.

FAN XIA <phoebexia@yahoo.com>

Charles W. Dunnett and Ajit C. Tamhane. A step-up multiple test procedure. Journal of the American Statistical Association, 87(417):162-170, 1992.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
#Compare group means of four treatment arms to a control arm (upper one-sided tests)
# Setting
k <- 4
mu <- 22 #assumed mean of each treatment arm
mu0 <- 20 #assumed mean of the control arm
n <- 100
n0 <- 80
sigma <- 5 #assumed population error variance
df <- n*k+n0-k-1 #consider the t distribution
# at one-sided significance level 0.05
# compare the testing powers of SD and SU Dunnett for rejecting at least one nulls
powSD <- powDT(r=1,k,mu,mu0,n,n0,"means",sigma=sigma,df=df,testcall="SD")
powSU <- powDT(r=1,k,mu,mu0,n,n0,"means",sigma=sigma,df=df,testcall="SU")
``` |

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