# Build a single-stage multi-arm clinical trial for a normally distributed primary outcome

Source: `R/build_ss_norm.R`
`build_ss_norm.Rd`

`build_ss_norm()` builds a single-stage multi-arm clinical trial design object assuming the primary outcome variable is normally distributed, like those returned by `des_ss_norm`.

    build_ss_norm(
      n = rep(98, 3),
      alpha = 0.025,
      beta = 0.1,
      delta1 = 0.5,
      delta0 = 0,
      sigma = rep(1, K + 1),
      correction = "dunnett",
      power = "marginal",
      summary = F
    )

## Arguments

n | A `numeric` vector indicating the chosen value for n, the sample size in each arm. Defaults to `rep(61, 3)`.
alpha | A `numeric` indicating the chosen value for α, the significance level. Defaults to `0.05`.
beta | A `numeric` indicating the chosen value for β, used in the definition of the desired power. Defaults to `0.2`.
delta1 | A `numeric` indicating the chosen value for δ1, the 'interesting' treatment effect. Defaults to `0.5`.
delta0 | A `numeric` indicating the chosen value for δ0, the 'uninteresting' treatment effect. Defaults to `0`.
sigma | A `numeric` vector indicating the chosen value for σ, the vector of the standard deviations of the responses in each arm. Defaults to `rep(1, K + 1)`.
correction | A `character` string indicating the chosen multiple comparison correction. Can be any of `"benjamini_hochberg"`, `"benjamini_yekutieli"`, `"bonferroni"`, `"dunnett"`, `"hochberg"`, `"holm_bonferroni"`, `"holm_sidak"`, `"none"`, `"sidak"`, and `"step_down_dunnett"`. Defaults to `"dunnett"`.
power | A `character` string indicating the chosen type of power to design the trial for. Can be one of `"conjunctive"`, `"disjunctive"`, and `"marginal"`. Defaults to `"marginal"`.
summary | A `logical` variable indicating whether a summary of the function's progress should be printed to the console. Defaults to `F`.

## Value

A `list` of class `"multiarm_des_ss_norm"` containing the following elements

- A `tibble` in the slot `$opchar` summarising the operating characteristics of the chosen design.

- A `numeric` in the slot `$N` specifying N, the trial's total required sample size.

- A `numeric` in the slot `$gamma` specifying the critical threshold for p-values, γ, below which null hypotheses would be rejected. Will be `NA` if `correction` is not a single-step testing procedure.

- A `numeric` vector in the slot `$gammaO` specifying the critical thresholds for ordered p-values, γ, to use with the chosen step-wise testing procedure. Will be `NA` if `correction` is not a step-wise testing procedure.

- A `matrix` in the slot `$CovZ` specifying the covariance matrix, Cov(Z), of the standardised test statistics.

- A `numeric` vector in the slot `$ratio` specifying the vector of allocation ratios, r.

- Each of the input variables.

## See also

`des_ss_norm`, `opchar_ss_norm`, `plot.multiarm_des_ss_norm`, `sim_ss_norm`.

## Examples

    # The design for the default parameters
    des     <- build_ss_norm()
    # Modifying the number of experimental treatments and the sample size in each
    # arm
    des_K_n <- build_ss_norm(n = rep(100, 4))