Adapt a cer design by dropping hypotheses, with a simplified strategy for redistributing weights

The weights of the dropped hypotheses are set to 0 and distributed proportionally to the previous weight to the other hypotheses. This means the weights "stay the same", they are only adapted to still sum up to 1. Note that this method does not lead to an adapted graph that is coherent with the actual weight distribution, this may lead to problems down the line

Usage

cer_alt_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, see cer_adapt()

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_alt_drop_hypotheses(design, c(TRUE, FALSE))
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
#> 
#> ── Interim test result ─────────────────────────────────────────────────────────
#> 
#> P-values of interim test are:
#> [1] 0.10 0.02
#> No Hypotheses were rejected at the interim
#> 
#> ── Adaptions from inital specification ─────────────────────────────────────────
#> 
#> New hypotheses weights
#> [1] 0 1
#>