Stack simulations_collection results vertically
stack_sims_vert(sim_map, target_dose = NULL, alpha = 0.05)object of type simulations_collection
optional integer vector, the dose of interest. All doses are analysed if omitted, which is the default.
confidence level for asymptotic normal confidence intervals. The default value is 0.05 to get 95 percent confidence intervals.
a data.frame
# In a five-dose scenario, we have assumed probabilities for Prob(tox):
true_prob_tox <- c(0.05, 0.10, 0.15, 0.18, 0.45)
# and Prob(eff):
true_prob_eff <- c(0.40, 0.50, 0.52, 0.53, 0.53)
# Let us compare two BOIN12 variants that differ in their stopping params:
designs <- list(
"BOIN12 v1" = get_boin12(num_doses = 5,
phi_t = 0.35, phi_e = 0.25,
u2 = 40, u3 = 60,
c_t = 0.95, c_e = 0.9) %>%
stop_at_n(n = 36),
"BOIN12 v2" = get_boin12(num_doses = 5,
phi_t = 0.35, phi_e = 0.25,
u2 = 40, u3 = 60,
c_t = 0.5, c_e = 0.5) %>%
stop_at_n(n = 36)
)
# For illustration we run only 10 iterates:
x <- simulate_compare(
designs,
num_sims = 10,
true_prob_tox,
true_prob_eff
)
#> Running BOIN12 v1
#> Running BOIN12 v2
stack_sims_vert(x)
#> # A tibble: 100 × 9
#> dose hit r n design .rate .se .l .u
#> <int> <lgl> <int> <int> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1 TRUE 1 1 BOIN12 v1 1 0 1 1
#> 2 1 FALSE 1 2 BOIN12 v1 0.5 0.354 -0.193 1.19
#> 3 1 TRUE 2 3 BOIN12 v1 0.667 0.272 0.133 1.20
#> 4 1 FALSE 2 4 BOIN12 v1 0.5 0.25 0.0100 0.990
#> 5 1 FALSE 2 5 BOIN12 v1 0.4 0.219 -0.0294 0.829
#> 6 1 FALSE 2 6 BOIN12 v1 0.333 0.192 -0.0439 0.711
#> 7 1 FALSE 2 7 BOIN12 v1 0.286 0.171 -0.0489 0.620
#> 8 1 FALSE 2 8 BOIN12 v1 0.25 0.153 -0.0501 0.550
#> 9 1 FALSE 2 9 BOIN12 v1 0.222 0.139 -0.0494 0.494
#> 10 1 FALSE 2 10 BOIN12 v1 0.2 0.126 -0.0479 0.448
#> # ℹ 90 more rows