Make a new CER trial design

Returns an object of class cer_design. This can be used for clinical trials with potential adaptions that are controlled for using the conditional error method.

Usage

cer_design(
  correlation = matrix(),
  weights = double(),
  alpha = double(),
  test_m = matrix(),
  alpha_spending_f = function() {
 },
  t = double(),
  seq_bonf = TRUE
)

Arguments

correlation

Correlation matrix describing the structure of the correlations between the different hypotheses, use NA for uncorrelated

weights

List of weights, measuring how important each hypothesis is

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)

t

numeric between 0 and 1 specifing the planned time fraction for the interim test

seq_bonf

to automatically reject hypotheses at the second stage if the sum of their PCER is greater 1

Value

An object of class cer_design

Examples

as <- function(x,t) 2-2*pnorm(qnorm(1-x/2)/sqrt(t))
design <- cer_design(
 correlation=rbind(H1=c(1, NA),
                   H2=c(NA, 1)),
 weights=c(2/3, 1/3),
 alpha=0.05,
 test_m=rbind(c(0, 1),
              c(1, 0)),
 alpha_spending_f=as,
 t=0.5)

design
#> A CER Design object, for testing 2 hypotheses at FWER 0.05.
#> 
#> ── Inital design specification ─────────────────────────────────────────────────
#> 
#> Hypotheses weights
#> [1] 0.6666667 0.3333333
#> 
#> Graph Transition Matrix
#>      [,1] [,2]
#> [1,]    0    1
#> [2,]    1    0
#> 
#> Correlation for parametric test
#>    [,1] [,2]
#> H1    1   NA
#> H2   NA    1
#> 
#> Interim test is planned at time fraction 0.5
#>