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

Source: `R/opchar_ss_norm.R`
`opchar_ss_norm.Rd`

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

    opchar_ss_norm(des = des_ss_norm(), tau, summary = FALSE)

## Arguments

des | A `list` of class `"multiarm_des_ss_norm"`, as returned by `build_ss_norm` or `des_ss_norm` (i.e., a single-stage multi-arm clinical trial design for a normally distributed outcome). Defaults to `des_ss_norm()`.
tau | A `matrix` whose rows indicate values of τ 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_norm`, `des_ss_norm`, `plot.multiarm_des_ss_norm`, `sim_ss_norm`.

## Examples

    # The operating characteristics for the default parameters
    opchar        <- opchar_ss_norm()
    # An A-optimal design
    des_A         <- des_ss_norm(ratio = "A")
    opchar_A      <- opchar_ss_norm(des_A)
    # Using the root-K allocation rule, modifying the desired type of power and
    # chosen multiple comparison correction, and specifying tau explicitly
    des_root_K    <- des_ss_norm(ratio      = rep(1/sqrt(2), 2),
                                 correction = "holm_bonferroni",
                                 power      = "disjunctive")
    opchar_root_K <- opchar_ss_norm(des_root_K, rbind(c(0, 0),
                                                      c(0.5, 0.5),
                                                      c(0.5, 0),
                                                      c(0, 0.5)))