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 1.0-1 Index]