Description Usage Arguments Details Value See Also Examples
View source: R/opchar_bayesfreq.R
opchar_bayesfreq()
supports the simultaneous evaluation of the
operating characteristics of multiple Bayesian-frequentist single-arm
clinical trial designs for a single binary primary endpoint, determined using
des_bayesfreq()
.
1 | opchar_bayesfreq(des, ..., k, mu, nu, pi, summary = F)
|
des |
An object of class |
... |
Additional objects of class |
k |
Calculations are performed conditional on the trial stopping in one
of the stages listed in vector |
mu |
A vector of the first Beta shape parameters to evaluate operating characteristics at. This will internally default to be the μ from the supplied designs if it left unspecified. |
nu |
A vector of the second Beta shape parameters to evaluate operating characteristics at. This will internally default to be the ν from the supplied designs if it left unspecified. |
pi |
A vector of response probabilities to evaluate operating characteristics at. This will internally default to be the π0 and π1 from the supplied designs if it is left unspecified. |
summary |
A logical variable indicating whether a summary of the function's progress should be printed to the console. |
Note that each of the supplied designs must have been designed for the same values of μ, ν, and π0.
For each value of mu,
nu, and
pi in
the supplied vectors mu,
nu, and
pi,
opchar_bayesfreq()
evaluates the Bayesian and frequentist power, ESS,
and other key characteristics, of each of the supplied designs.
Calculations are performed conditional on the trial stopping in one of the
stages specified using the input (vector) k
.
A list of class "sa_opchar_bayesfreq"
containing the following
elements
A tibble in the slot $opchar_bayes
summarising the Bayesian
operating characteristics of the supplied designs.
A tibble in the slot $opchar_freq
summarising the frequentist
operating characteristics of the supplied designs.
Each of the input variables as specified, subject to internal modification.
des_bayesfreq
, and their associated plot
family
of functions.
1 2 3 4 5 6 7 | # Find the optimal two-stage Bayesian-frequentist design for the default
# parameters
des <- des_bayesfreq()
# Determine operating characteristics for a range of mu, nu, and pi
opchar <- opchar_bayesfreq(des, mu = seq(0.05, 0.2, length.out = 10),
nu = seq(0.45, 1.8, length.out = 100),
pi = seq(0, 1, by = 0.01))
|
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