Calculate power for multiple contrast test
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
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.