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

Source: `R/opchar_ss_pois.R`
`opchar_ss_pois.Rd`

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

    opchar_ss_pois(des = des_ss_pois(), lambda, summary = FALSE)

## Arguments

des | A `list` of class `"multiarm_des_ss_pois"`, as returned by `des_ss_pois` (i.e., a single-stage multi-arm clinical trial design for a Poisson distributed outcome). Defaults to `des_ss_pois()`.
lambda | A `matrix` whose rows indicate values of λ 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_pois`, `des_ss_pois`, `plot.multiarm_des_ss_pois`, `sim_ss_pois`.

## Examples

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