Description Usage Arguments Details Value Examples
View source: R/fromQuantiles.R
This function constructs a minimally informative prior, which is captured in
a LogisticNormal
(or
LogisticLogNormal
) object.
1 2  MinimalInformative(dosegrid, refDose, threshmin = 0.2, threshmax = 0.3,
probmin = 0.05, probmax = 0.05, ...)

dosegrid 
the dose grid 
refDose 
the reference dose 
threshmin 
Any toxicity probability above this threshold would
be very unlikely (see 
threshmax 
Any toxicity probability below this threshold would
be very unlikely (see 
probmin 
the prior probability of exceeding 
probmax 
the prior probability of being below 
... 
additional arguments for computations, see

Based on the proposal by Neuenschwander et al (2008, Statistics in
Medicine), a minimally informative prior distribution is constructed. The
required key input is the minimum (d_{1} in the notation of the
Appendix A.1 of that paper) and the maximum value (d_{J}) of the dose
grid supplied to this function. Then threshmin
is the probability
threshold q_{1}, such that any probability of DLT larger than
q_{1} has only 5% probability. Therefore q_{1} is the 95%
quantile of the beta distribution and hence p_{1} = 0.95. Likewise,
threshmax
is the probability threshold q_{J}, such that any
probability of DLT smaller than q_{J} has only 5% probability
(p_{J} = 0.05). The probabilities 1  p_{1} and p_{J} can be
controlled with the arguments probmin
and probmax
, respectively.
Subsequently, for all doses supplied in the
dosegrid
argument, beta distributions are set up from the assumption
that the prior medians are linear in logdose on the logit scale, and
Quantiles2LogisticNormal
is used to transform the resulting
quantiles into an approximating LogisticNormal
(or
LogisticLogNormal
) model. Note that the reference dose
is not required for these computations.
see Quantiles2LogisticNormal
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  # Setting up a minimal informative prior
# max.time is quite small only for the purpose of showing the example. They
# should be increased for a real case.
set.seed(132)
coarseGrid < c(0.1, 10, 30, 60, 100)
minInfModel < MinimalInformative(dosegrid = coarseGrid,
refDose=50,
threshmin=0.2,
threshmax=0.3,
control=## for real case: leave out control
list(max.time=0.1))
# Plotting the result
matplot(x=coarseGrid,
y=minInfModel$required,
type="b", pch=19, col="blue", lty=1,
xlab="dose",
ylab="prior probability of DLT")
matlines(x=coarseGrid,
y=minInfModel$quantiles,
type="b", pch=19, col="red", lty=1)
legend("right",
legend=c("quantiles", "approximation"),
col=c("blue", "red"),
lty=1,
bty="n")

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