Adapt a cer design by dropping hypotheses

The weights of the dropped hypotheses are set to 0 and distributed according to the prespecified graph. However, the time fraction is not adapted, this needs to be done manually if desired.

Usage

cer_drop_hypotheses(design, hypotheses, adapt_bounds = TRUE)

Arguments

design

cer_design object

hypotheses

vector of booleans indicating for each hypotheses if it should be dropped

adapt_bounds

Adapt the bounds for rejecting a hypotheses to keep the FWER with the new adaptions. If doing multiple adaptions, it is enough to adapt bounds only for the last one, or call adapt_bounds() manually after.

Value

design with specified hypotheses dropped (so TRUE means the hypothesis is dropped)

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 <- cer_interim_test(design, c(0.1, 0.02))

design <- cer_drop_hypotheses(design, c(TRUE, FALSE))
design
#> A CER Design object, for testing 2 hypotheses at FWER 0.05.
#> 
#> ── An interim test has been performed. ─────────────────────────────────────────
#> No Hypotheses were rejected at the interim.
#> ── The following characteristics have been adapted: ────────────────────────────
#> • Hypotheses weights
#> • Graph Transition Matrix
#> ── No final test has been performed yet ────────────────────────────────────────