Prints the decision intervals of qdm_boundary as up to
three tables, each with one row per condition (for example, the number of
control responders or the treatment group size) and one column per
decision. The first table gives the range of the observed quantity
(numbers of responders or events, or observed means), the second the
corresponding observed response rates or milestone survival
probabilities, and the last the observed treatment effects. For example,
the entry "10-20" in the column Go means that 10 to 20
responders lead to a Go decision. The object itself
is a data frame with one row per interval (see qdm_boundary).
Usage
# S3 method for class 'qdm_boundary'
print(x, digits = 4L, n = 20L, ...)Examples
des <- design_binary(
n_t = 12, n_c = 12,
prior_t = prior_beta(0.5, 0.5), prior_c = prior_beta(0.5, 0.5),
criterion = criterion_posterior(theta_tv = 0.2, theta_mav = 0.1,
gamma_go = 0.5, gamma_nogo = 0.8)
)
b <- qdm_boundary(des)
print(b, n = 5)
#> Decision boundary of a design with one binary endpoint
#> n_t = 12, n_c = 12
#>
#> Responders in the treatment group (y_t)
#> y_c NoGo Gray Go
#> 0 0 1-2 3-12
#> 1 0-1 2-3 4-12
#> 2 0-1 2-4 5-12
#> 3 0-2 3-5 6-12
#> 4 0-3 4-6 7-12
#>
#> Observed response rate (y_t / n_t)
#> y_c NoGo Gray Go
#> 0 0.0% 8.3% to 16.7% 25.0% to 100.0%
#> 1 0.0% to 8.3% 16.7% to 25.0% 33.3% to 100.0%
#> 2 0.0% to 8.3% 16.7% to 33.3% 41.7% to 100.0%
#> 3 0.0% to 16.7% 25.0% to 41.7% 50.0% to 100.0%
#> 4 0.0% to 25.0% 33.3% to 50.0% 58.3% to 100.0%
#>
#> Observed difference in response rates (y_t / n_t - y_c / n_c)
#> y_c NoGo Gray Go
#> 0 0 0.08333 to 0.1667 0.25 to 1
#> 1 -0.08333 to 0 0.08333 to 0.1667 0.25 to 0.9167
#> 2 -0.1667 to -0.08333 0 to 0.1667 0.25 to 0.8333
#> 3 -0.25 to -0.08333 0 to 0.1667 0.25 to 0.75
#> 4 -0.3333 to -0.08333 0 to 0.1667 0.25 to 0.6667
#> ... 8 more values of y_c (see the data frame)
# The intervals as a data frame
head(as.data.frame(b))
#> n_t n_c y_c decision from to rate_from rate_to effect_from effect_to
#> 1 12 12 0 NoGo 0 0 0.00000000 0.00000000 0.00000000 0.0000000
#> 2 12 12 0 Gray 1 2 0.08333333 0.16666667 0.08333333 0.1666667
#> 3 12 12 0 Go 3 12 0.25000000 1.00000000 0.25000000 1.0000000
#> 4 12 12 1 NoGo 0 1 0.00000000 0.08333333 -0.08333333 0.0000000
#> 5 12 12 1 Gray 2 3 0.16666667 0.25000000 0.08333333 0.1666667
#> 6 12 12 1 Go 4 12 0.33333333 1.00000000 0.25000000 0.9166667