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

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

1 2 3 |

`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`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | ```
## 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)
``` |

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