Analytically determine the operating characteristics of a single-stage multi-arm clinical trial for a Bernoulli distributed primary outcome

opchar_ss_bern() determines the operating characteristics of a specified single-stage multi-arm clinical trial design assuming the primary outcome variable is Bernoulli distributed, for given values of the true treatment effects, using multivariate normal integration.

opchar_ss_bern(des = des_ss_bern(), pi, summary = FALSE)

Arguments

des	A list of class "multiarm_des_ss_bern", as returned by des_ss_bern (i.e., a single-stage multi-arm clinical trial design for a Poisson distributed outcome). Defaults to des_ss_bern().
pi	A matrix whose rows indicate values of π at which to evaluate the operating characteristics. Defaults internally to the global null, global alternative, and each of the least favourable configurations, for the specified design des.
summary	A logical variable indicating whether a summary of the function's progress should be printed to the console. Defaults to FALSE.

Value

A list containing the following elements

• A tibble in the slot $opchar giving the determined operating characteristics.

• Each of the input variables.

See also

build_ss_bern, des_ss_bern, plot.multiarm_des_ss_bern, sim_ss_bern.

Examples

# The operating characteristics for the default parameters
opchar        <- opchar_ss_bern()
# An A-optimal design
des_A         <- des_ss_bern(ratio = "A")
opchar_A      <- opchar_ss_bern(des_A)
# Using the root-K allocation rule, modifying the desired type of power and
# chosen multiple comparison correction, and specifying pi explicitly
des_root_K    <- des_ss_bern(ratio      = rep(1/sqrt(2), 2),
                             correction = "holm_bonferroni",
                             power      = "disjunctive")
opchar_root_K <- opchar_ss_bern(des_root_K, rbind(c(0.3, 0.3, 0.3),
                                                  c(0.3, 0.5, 0.5),
                                                  c(0.3, 0.5, 0.3),
                                                  c(0.3, 0.3, 0.5)))