MCTtest: Performs multiple contrast test

In DoseFinding: Planning and Analyzing Dose Finding Experiments

Performs multiple contrast test

Description

This function performs a multiple contrast test. The contrasts are either directly specified in ‘⁠contMat⁠’ or optimal contrasts derived from the ‘⁠models⁠’ argument. The directionality of the data (i.e. whether an increase or decrease in the response variable is beneficial is inferred from the ‘⁠models⁠’ object, see Mods()).

Usage

MCTtest(
  dose,
  resp,
  data = NULL,
  models,
  S = NULL,
  type = c("normal", "general"),
  addCovars = ~1,
  placAdj = FALSE,
  alpha = 0.025,
  df = NULL,
  critV = NULL,
  pVal = TRUE,
  alternative = c("one.sided", "two.sided"),
  na.action = na.fail,
  mvtcontrol = mvtnorm.control(),
  contMat = NULL
)

Arguments

dose, resp	Either vectors of equal length specifying dose and response values, or names of variables in the data frame specified in ‘⁠data⁠’.
data	Data frame containing the variables referenced in dose and resp if ‘⁠data⁠’ is not specified it is assumed that ‘⁠dose⁠’ and ‘⁠resp⁠’ are variables referenced from data (and no vectors)
models	An object of class ‘⁠Mods⁠’, see Mods() for details
S	The covariance matrix of ‘⁠resp⁠’ when ‘⁠type = "general"⁠’, see Description.
type	Determines whether inference is based on an ANCOVA model under a homoscedastic normality assumption (when ‘⁠type = "normal"⁠’), or estimates at the doses and their covariance matrix and degrees of freedom are specified directly in ‘⁠resp⁠’, ‘⁠S⁠’ and ‘⁠df⁠’. See also fitMod() and \insertCitepinheiro2014DoseFinding.
addCovars	Formula specifying additive linear covariates (for ‘⁠type = "normal"⁠’)
placAdj	Logical, if true, it is assumed that placebo-adjusted estimates are specified in ‘⁠resp⁠’ (only possible for ‘⁠type = "general"⁠’).
alpha	Significance level for the multiple contrast test
df	Specify the degrees of freedom to use in case ‘⁠type = "general"⁠’. If this argument is missing ‘⁠df = Inf⁠’ is used (which corresponds to the multivariate normal distribution). For type = "normal" the degrees of freedom deduced from the AN(C)OVA fit are used and this argument is ignored.
critV	Supply a pre-calculated critical value. If this argument is NULL, no critical value will be calculated and the test decision is based on the p-values. If ‘⁠critV = TRUE⁠’ the critical value will be calculated.
pVal	Logical determining, whether p-values should be calculated.
alternative	Character determining the alternative for the multiple contrast trend test.
na.action	A function which indicates what should happen when the data contain NAs.
mvtcontrol	A list specifying additional control parameters for the ‘⁠qmvt⁠’ and ‘⁠pmvt⁠’ calls in the code, see also mvtnorm.control() for details.
contMat	Contrast matrix to apply to the ANCOVA dose-response estimates. The contrasts need to be in the columns of the matrix (i.e. the column sums need to be 0).

Details

For ‘⁠type = "normal"⁠’ an ANCOVA model based on a homoscedastic normality assumption (with additive covariates specified in ‘⁠addCovars⁠’) is fitted.

For ‘⁠type = "general"⁠’ it is assumed multivariate normally distributed estimates are specified in ‘⁠resp⁠’ with covariance given by ‘⁠S⁠’, and the contrast test statistic is calculated based on this assumption. Degrees of freedom specified in ‘⁠df⁠’.

Integrals over the multivariate t and multivariate normal distribution are calculated using the ‘⁠mvtnorm⁠’ package.

Value

An object of class MCTtest, a list containing the output.

Author(s)

Bjoern Bornkamp

References

\insertRef

hothorn2008DoseFinding

\insertRef

pinheiro2014DoseFinding

See Also

powMCT(), optContr()

Examples

## example without covariates
data(biom)
## define shapes for which to calculate optimal contrasts
modlist <- Mods(emax = 0.05, linear = NULL, logistic = c(0.5, 0.1),
                linInt = c(0, 1, 1, 1), doses = c(0, 0.05, 0.2, 0.6, 1))
m1 <- MCTtest(dose, resp, biom, models=modlist)
## now calculate critical value (but not p-values)
m2 <- MCTtest(dose, resp, biom, models=modlist, critV = TRUE, pVal = FALSE)
## now hand over critical value
m3 <- MCTtest(dose, resp, biom, models=modlist, critV = 2.24)

## example with covariates
data(IBScovars)
modlist <- Mods(emax = 0.05, linear = NULL, logistic = c(0.5, 0.1),
                linInt = c(0, 1, 1, 1), doses = c(0, 1, 2, 3, 4))
MCTtest(dose, resp, IBScovars, models = modlist, addCovars = ~gender)

## example using general approach (fitted on placebo-adjusted scale)
ancMod <- lm(resp~factor(dose)+gender, data=IBScovars)
## extract estimates and information to feed into MCTtest
drEst <- coef(ancMod)[2:5]
vc <- vcov(ancMod)[2:5, 2:5]
doses <- 1:4
MCTtest(doses, drEst, S = vc, models = modlist, placAdj = TRUE,
        type = "general", df = Inf)

## example with general alternatives handed over
data(biom)
## calculate contrast matrix for the step-contrasts
## represent them as linInt models
models <- Mods(linInt=rbind(c(1,1,1,1),
                            c(0,1,1,1),
                            c(0,0,1,1),
                            c(0,0,0,1)),
                doses=c(0,0.05,0.2,0.6,1))
plot(models)
## now calculate optimal contrasts for these means
## use weights from actual sample sizes
weights <- as.numeric(table(biom$dose))
contMat <- optContr(models, w = weights)
## plot contrasts
plot(contMat)
## perform multiple contrast test
MCTtest(dose, resp, data=biom, contMat = contMat)

## example for using the Dunnett contrasts
## Dunnett contrasts
doses <- sort(unique(biom$dose))
contMat <- rbind(-1, diag(4))
rownames(contMat) <- doses
colnames(contMat) <- paste("D", doses[-1], sep="")
MCTtest(dose, resp, data=biom, contMat = contMat)

DoseFinding documentation built on Oct. 16, 2026, 5:08 p.m.