powMCT {DoseFinding} | R Documentation |

Calculate power for a multiple contrast test for a set of specified alternatives.

```
powMCT(contMat, alpha = 0.025, altModels, n, sigma, S, placAdj=FALSE,
alternative = c("one.sided", "two.sided"), df, critV,
control = mvtnorm.control())
```

`contMat` |
Contrast matrix to use. The individual contrasts should be saved in the columns of the matrix |

`alpha` |
Significance level to use |

`altModels` |
An object of class ‘Mods’, defining the mean vectors under which the power should be calculated |

`n, sigma, S` |
Either a vector ‘n’ and ‘sigma’ or ‘S’ need to be
specified. When ‘n’ and ‘sigma’ are specified it is
assumed computations are made for a normal homoscedastic ANOVA model
with group sample sizes given by ‘n’ and residual standard
deviation ‘sigma’, i.e. the covariance matrix used for the
estimates is thus When ‘S’ is specified this will be used as covariance matrix for the estimates. |

`placAdj` |
Logical, if true, it is assumed that the standard deviation or variance
matrix of the placebo-adjusted estimates are specified in
‘sigma’ or ‘S’, respectively. The contrast matrix has to be
produced on placebo-adjusted scale, see |

`alternative` |
Character determining the alternative for the multiple contrast trend test. |

`df` |
Degrees of freedom to assume in case ‘S’ (a general covariance matrix) is specified. When ‘n’ and ‘sigma’ are specified the ones from the corresponding ANOVA model are calculated. |

`critV` |
Critical value, if equal to ‘TRUE’ the critical value will be calculated. Otherwise one can directly specify the critical value here. |

`control` |
A list specifying additional control parameters for the ‘qmvt’ and ‘pmvt’ calls in the code, see also ‘mvtnorm.control’ for details. |

Numeric containing the calculated power values

Bjoern Bornkamp

Pinheiro, J. C., Bornkamp, B., and Bretz, F. (2006). Design and analysis of dose finding studies
combining multiple comparisons and modeling procedures, *Journal of Biopharmaceutical
Statistics*, **16**, 639–656

`powN`

, `sampSizeMCT`

, `MCTtest`

,
`optContr`

, `Mods`

```
## look at power under some dose-response alternatives
## first the candidate models used for the contrasts
doses <- c(0,10,25,50,100,150)
## define models to use as alternative
fmodels <- Mods(linear = NULL, emax = 25,
logistic = c(50, 10.88111), exponential= 85,
betaMod=rbind(c(0.33,2.31),c(1.39,1.39)),
doses = doses, addArgs=list(scal = 200),
placEff = 0, maxEff = 0.4)
## plot alternatives
plot(fmodels)
## power for to detect a trend
contMat <- optContr(fmodels, w = 1)
powMCT(contMat, altModels = fmodels, n = 50, alpha = 0.05, sigma = 1)
## Not run:
## power under the Dunnett test
## contrast matrix for Dunnett test with informative names
contMatD <- rbind(-1, diag(5))
rownames(contMatD) <- doses
colnames(contMatD) <- paste("D", doses[-1], sep="")
powMCT(contMatD, altModels = fmodels, n = 50, alpha = 0.05, sigma = 1)
## now investigate power of the contrasts in contMat under "general" alternatives
altFmods <- Mods(linInt = rbind(c(0, 1, 1, 1, 1),
c(0.5, 1, 1, 1, 0.5)),
doses=doses, placEff=0, maxEff=0.5)
plot(altFmods)
powMCT(contMat, altModels = altFmods, n = 50, alpha = 0.05, sigma = 1)
## now the first example but assume information only on the
## placebo-adjusted scale
## for balanced allocations and 50 patients with sigma = 1 one obtains
## the following covariance matrix
S <- 1^2/50*diag(6)
## now calculate variance of placebo adjusted estimates
CC <- cbind(-1,diag(5))
V <- (CC)%*%S%*%t(CC)
linMat <- optContr(fmodels, doses = c(10,25,50,100,150),
S = V, placAdj = TRUE)
powMCT(linMat, altModels = fmodels, placAdj=TRUE,
alpha = 0.05, S = V, df=6*50-6) # match df with the df above
## End(Not run)
```

[Package *DoseFinding* version 1.0-2 Index]