Make a new multiarm CER trial design — multiarm_cer

Returns an object of class multiarm_cer_design. This can be used for clinical trials with multiple arms and control groups that might have adaptations controlled for using the conditional error method.

Usage

multiarm_cer_design(
  controls = integer(),
  treatment_assoc = integer(),
  n_controls = integer(),
  n_treatments = integer(),
  weights = double(),
  t = double(),
  alpha = double(),
  test_m = matrix(),
  alpha_spending_f = function() {
 },
  seq_bonf = TRUE
)

Arguments

controls: Number of control groups
treatment_assoc: Vector of integer, corresponding to the number of the control group for each treatment group. The length determines the number of treatment groups
n_controls: Integer (or vector of integers) determining the number of patients in each control group
n_treatments: Integer (or vector of integers) determining the number of patients in each treatment group
weights: List of weights, measuring how important each hypothesis is
t: information fraction, at which fraction of assigned people will the interim analysis happen
alpha: Single number, measuring what total alpha should be spent on the FWER
test_m: Transition matrix describing the graph for the closed test procedure to test the hypotheses
alpha_spending_f: alpha spending function, taking parameters alpha (for overall spent alpha) and t (information fraction at interim test)
seq_bonf: automatically reject hypotheses at the second stage if the sum of their PCER is greater 1

Value

An object of class multiarm_cer_design

Examples

as <- function(x,t) 2-2*pnorm(qnorm(1-x/2)/sqrt(t))
design <- multiarm_cer_design(
 controls = 1,
 treatment_assoc = c(1,1),
 n_controls = 50,
 n_treatments = 50,
 weights = c(0.5, 0.5),
 alpha = 0.05,
 test_m = rbind(c(0, 1),
              c(1, 0)),
 alpha_spending_f = as,
 t = 0.5)

design
#> A Multi-arm Design object, for testing 2 hypotheses at FWER 0.05.
#> 
#> ── Inital design specification ─────────────────────────────────────────────────
#> 
#> Hypotheses weights
#> [1] 0.5 0.5
#> 
#> Graph Transition Matrix
#>      [,1] [,2]
#> [1,]    0    1
#> [2,]    1    0
#> 
#> Correlation for parametric test
#>      [,1] [,2]
#> [1,]  1.0  0.5
#> [2,]  0.5  1.0
#> 
#> Number of control groups:
#> [1] 1
#> 
#> Treatment-to-control assignments (per treatment arm):
#> [1] 1 1
#> 
#> Planned sample sizes per control group:
#> [1] 50
#> 
#> Planned sample sizes per treatment group:
#> [1] 50 50
#> 
#> Interim test is planned at time fraction 0.5
#>