Description Usage Arguments Value Examples
Like the powMCT function, this function allows the user to calculate power
for a multiple contrast test for a set of specified alternatives for the
general case, but without specifying and S matrix. The user supplies the
patient allocation, the alternative models, and any parameters needed for the
distribution family (e.g. the dispersion parameter for the negative binomial
distribution). The function works by calculating the μ and S for each
model in the alternative models and supplying the calculated values to the
powMCT function from the DoseFinding package, forwarding relevant arguments.
This function also allows a new Ntype, namely 'actual'. If nSample is a
vector and Ntype is 'actual', the function interprets nSample to be the exact
patient allocation. This is useful for slightly modifying patient allocation
and avoiding messy ratios.
Furthermore, the function also accepts a
theoResp and doses argument, which together describe the theoretical
doseresponse relationship. The returned power is the probability of
accepting at least one of the models specified in altModels.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  powMCTGen(
nSample,
family = c("negative binomial", "binomial", "poisson"),
link = c("log", "logit", "sqrt", "probit", "cauchit", "cloglog", "identity",
"risk ratio", "log risk ratio"),
modelPar = NULL,
placEff = NULL,
theoResp = NULL,
doses = NULL,
Ntype = c("arm", "total", "actual"),
alRatio = NULL,
altModels,
alpha = 0.025,
df = NULL,
critV = TRUE,
alternative = c("one.sided", "two.sided"),
verbose = FALSE,
offset = NULL
)

nSample 
An integer if 
family 
A character string containing the error distribution to be used in the model. 
link 
A character string for the model link function. 
modelPar 
A numeric vector containing the additional parameters for the family argument. If the family is negative binomial, the dispersion parameter should be supplied. If the family is binomial, no model parameter should be supplied. 
placEff 
A numeric value specifying the mean response at the placebo
This is required if 
theoResp 
A numerical vector of theoretical response values, on the transformed scale (e.g. on the logscale for the negative binomial family). This should be the same length as the doses argument. 
doses 
A numerical vector of doses, corresponding to the theoretical response values provided. 
Ntype 
One of 'arm', 'total', or 'actual'. See documentation for

alRatio 
Vector describing the relative patient allocations to the dose
groups up to proportionality, e.g. 
altModels 
An object of class 
alpha 
Significance level to use. 
df 
Degrees of freedom to assume. 
critV 
Critical value, if equal to 
alternative 
Character determining the alternative for the multiple contrast trend test. 
verbose 
A logical specifying whether the patient allocation should be printed, in addition to the results. 
offset 
A positive numeric value specifying the offset term for the negative binomial distribution. If offset = 1 (the default), then the offset has no effect. Theoretically, the offset should be a numeric vector the same length as the number of observations, but for planning purposes, it is unlikely to know the individual offsets in advance. 
Numeric containing the calculated power values
1 2 3 4 5 6 7 8 9 10 11 12 13  dose.vec = c(0, 5, 10, 20, 30, 40)
models.full = Mods(doses = dose.vec, linear = NULL,
sigEmax = rbind(c(9, 2), c(6, 3)),
emax = 0.8,
quadratic = 0.02,
placEff = 0, maxEff = 2)
## Calculate the power using the responses and doses specified in Mods
powMCTGen(30, 'negative binomial', 'log', modelPar = 0.1, Ntype = 'arm',
alpha = 0.05, altModels = models.full)
## Calculate the power at theoretical doseresponse values
powMCTGen(30, 'negative binomial', 'log', modelPar = 0.1,
theoResp = c(0, 0.01, 0.02, 1, 1.6, 1.8), doses = c(0, 10, 20, 30, 40, 50),
alpha = 0.05, altModels = models.full)

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