make_decision_trial.RdGiven a res_list object, checks the supplied decision criteria and saves the results in the res_list file.
make_decision_trial(
res_list,
which_cohort,
test_strat = 3,
sharing_type = "all",
Bayes_Sup = NULL,
Bayes_Fut = NULL,
Bayes_SA_Sup = NULL,
Bayes_SA_Fut = NULL,
w = 0.5,
P_Sup = NULL,
P_Fut = NULL,
Est_Sup_Fut = NULL,
CI_Sup_Fut = NULL,
interim,
beta_prior = 0.5,
...
)List item containing individual cohort trial results so far in a format used by the other functions in this package
Current cohort that should be evaluated
Testing strategy used; 1 = Combo vs. both monos, 2 = 1 + Add-on Mono vs. Placebo, 3 = 2 + Backbone mono vs. placebo
What backbone and placebo data should be used for comparisons; Default is "all". Other options are "concurrent" or "dynamic" or "cohort".
List of matrices with rows corresponding to number of multiple Bayesian posterior two-arm combination criteria for superiority
List of matrices with rows corresponding to number of multiple Bayesian posterior two-arm combination criteria for futility
List of matrices with rows corresponding to number of multiple Bayesian posterior single-arm combination criteria for superiority
List of matrices with rows corresponding to number of multiple Bayesian posterior single-arm combination criteria for futility
If dynamic borrowing, what is the prior choice for w. Default is 0.5.
List with sublists corresponding to number of multiple frequentist test-based combination criteria for superiority
List with sublists corresponding to number of multiple frequentist test-based combination criteria for futility
List with sublists corresponding to number of multiple point estimate based combination criteria for superiority and futility
List with sublists corresponding to number of multiple confidence interval based combination criteria for superiority and futility
Is the analysis conducted an interim or a final analysis?
Prior parameter for all Beta Distributions. Default is 0.5.
Further arguments inherited from upper layer functions
List containing original res_list and results of decision rules
# Example 1
res_list <- list(c(list(decision = rep("none", 2), alloc_ratio = c(1,1,1,1),
n_thresh = c(Inf, 210)),
rep(list(list(rr = NULL, resp_bio = NULL, resp_hist = NULL, n = NULL)), 4)))
names(res_list)[1] <- paste0("Cohort", 1)
names(res_list[[1]])[4:7] <- c("Comb", "Mono", "Back", "Plac")
res_list[[1]][[4]]$rr <- 0.2
res_list[[1]][[5]]$rr <- 0.15
res_list[[1]][[6]]$rr <- 0.15
res_list[[1]][[7]]$rr <- 0.10
r141 <- rbinom(1, 70, prob = res_list[[1]][[4]]$rr)
res_list[[1]][[4]]$resp_bio <- gtools::permute(c(rep(1, r141), rep(0, 70 - r141)))
r151 <- rbinom(1, 70, prob = res_list[[1]][[5]]$rr)
res_list[[1]][[5]]$resp_bio <- gtools::permute(c(rep(1, r151), rep(0, 70 - r151)))
r161 <- rbinom(1, 70, prob = res_list[[1]][[6]]$rr)
res_list[[1]][[6]]$resp_bio <- gtools::permute(c(rep(1, r161), rep(0, 70 - r161)))
r171 <- rbinom(1, 70, prob = res_list[[1]][[7]]$rr)
res_list[[1]][[7]]$resp_bio <- gtools::permute(c(rep(1, r171), rep(0, 70 - r171)))
r142 <- rbinom(1, 70, prob = res_list[[1]][[4]]$rr)
res_list[[1]][[4]]$resp_hist <- gtools::permute(c(rep(1, r142), rep(0, 70 - r142)))
r152 <- rbinom(1, 70, prob = res_list[[1]][[5]]$rr)
res_list[[1]][[5]]$resp_hist <- gtools::permute(c(rep(1, r152), rep(0, 70 - r152)))
r162 <- rbinom(1, 70, prob = res_list[[1]][[6]]$rr)
res_list[[1]][[6]]$resp_hist <- gtools::permute(c(rep(1, r162), rep(0, 70 - r162)))
r172 <- rbinom(1, 70, prob = res_list[[1]][[7]]$rr)
res_list[[1]][[7]]$resp_hist <- gtools::permute(c(rep(1, r172), rep(0, 70 - r172)))
res_list[[1]][[4]]$n <- rep(1, 70)
res_list[[1]][[5]]$n <- rep(1, 70)
res_list[[1]][[6]]$n <- rep(1, 70)
res_list[[1]][[7]]$n <- rep(1, 70)
# Comparison Combo vs Mono
Bayes_Sup1 <- matrix(nrow = 3, ncol = 3)
Bayes_Sup1[1,] <- c(0.00, 0.95, 0.90)
Bayes_Sup1[2,] <- c(0.10, 0.80, 0.75)
Bayes_Sup1[3,] <- c(0.15, 0.50, 1.00)
# Comparison Combo vs Backbone
Bayes_Sup2 <- matrix(nrow = 3, ncol = 3)
Bayes_Sup2[1,] <- c(0.00, 0.95, 0.90)
Bayes_Sup2[2,] <- c(NA, NA, NA)
Bayes_Sup2[3,] <- c(NA, NA, NA)
# Comparison Mono vs Placebo
Bayes_Sup3 <- matrix(nrow = 3, ncol = 3)
Bayes_Sup3[1,] <- c(0.00, 0.95, 0.90)
Bayes_Sup3[2,] <- c(0.10, 0.80, 0.75)
Bayes_Sup3[3,] <- c(NA, NA, NA)
#' # Comparison Backbone vs Placebo
Bayes_Sup4 <- matrix(nrow = 3, ncol = 3)
Bayes_Sup4[1,] <- c(0.00, 0.95, 0.90)
Bayes_Sup4[2,] <- c(0.10, 0.80, 0.75)
Bayes_Sup4[3,] <- c(NA, NA, NA)
Bayes_Sup <- list(list(Bayes_Sup1, Bayes_Sup2, Bayes_Sup3, Bayes_Sup4),
list(Bayes_Sup1, Bayes_Sup2, Bayes_Sup3, Bayes_Sup4))
sharing_type <- "all"
interim <- TRUE
which_cohort <- 1
missing_prob <- 0.5
seed_missing <- 100
make_decision_trial(
res_list = res_list, which_cohort = which_cohort,
interim = interim, missing_prob = missing_prob,
Bayes_Sup = Bayes_Sup, sharing_type = sharing_type,
seed_missing = seed_missing,
)
#> $Cohort1
#> $Cohort1$decision
#> [1] "CONTINUE" "none"
#>
#> $Cohort1$alloc_ratio
#> [1] 1 1 1 1
#>
#> $Cohort1$n_thresh
#> [1] Inf 210
#>
#> $Cohort1$Comb
#> $Cohort1$Comb$rr
#> [1] 0.2
#>
#> $Cohort1$Comb$resp_bio
#> [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 1 0 1 0 1 0 1 0 1 1 0 0 1 0 0
#> [39] 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0
#>
#> $Cohort1$Comb$resp_hist
#> [1] 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0
#> [39] 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
#>
#> $Cohort1$Comb$n
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [39] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>
#>
#> $Cohort1$Mono
#> $Cohort1$Mono$rr
#> [1] 0.15
#>
#> $Cohort1$Mono$resp_bio
#> [1] 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 1 0
#> [39] 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0
#>
#> $Cohort1$Mono$resp_hist
#> [1] 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1
#> [39] 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>
#> $Cohort1$Mono$n
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [39] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>
#>
#> $Cohort1$Back
#> $Cohort1$Back$rr
#> [1] 0.15
#>
#> $Cohort1$Back$resp_bio
#> [1] 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [39] 0 0 0 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 0
#>
#> $Cohort1$Back$resp_hist
#> [1] 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0
#> [39] 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
#>
#> $Cohort1$Back$n
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [39] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>
#>
#> $Cohort1$Plac
#> $Cohort1$Plac$rr
#> [1] 0.1
#>
#> $Cohort1$Plac$resp_bio
#> [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
#> [39] 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>
#> $Cohort1$Plac$resp_hist
#> [1] 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [39] 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
#>
#> $Cohort1$Plac$n
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [39] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>
#>
#> $Cohort1$prob_sup1
#> [1] 0.8514739 0.8514739 0.9983249 0.9983249
#>
#> $Cohort1$prob_sup2
#> [1] 0.3303582 NA 0.7939001 0.7939001
#>
#> $Cohort1$prob_sup3
#> [1] 0.1196629 NA NA NA
#>
#> $Cohort1$sup_interim_list
#> $Cohort1$sup_interim_list[[1]]
#> [,1] [,2] [,3] [,4]
#> [1,] FALSE FALSE TRUE TRUE
#> [2,] FALSE NA FALSE FALSE
#> [3,] FALSE NA NA NA
#>
#>
#> $Cohort1$fut_interim_list
#> list()
#>
#> $Cohort1$prom_interim_list
#> $Cohort1$prom_interim_list[[1]]
#> [,1] [,2] [,3] [,4]
#> [1,] FALSE FALSE FALSE FALSE
#> [2,] FALSE NA TRUE TRUE
#> [3,] FALSE NA NA NA
#>
#>
#> $Cohort1$sup_interim
#> [1] FALSE
#>
#> $Cohort1$fut_interim
#> [1] FALSE
#>
#> $Cohort1$prom_interim
#> [1] FALSE
#>
#>
# Multiple decision rules
# Vergleich Combo vs Mono
Bayes_Fut1 <- matrix(nrow = 1, ncol = 2)
Bayes_Fut1[1,] <- c(NA, NA)
# Vergleich Combo vs Backbone
Bayes_Fut2 <- matrix(nrow = 1, ncol = 2)
Bayes_Fut2[1,] <- c(NA, NA)
# Vergleich Mono vs Placebo
Bayes_Fut3 <- matrix(nrow = 1, ncol = 2)
Bayes_Fut3[1,] <- c(0.00, 0.60)
Bayes_Fut4 <- matrix(nrow = 1, ncol = 2)
Bayes_Fut4[1,] <- c(0.00, 0.60)
Bayes_Fut <- list(list(Bayes_Fut1, Bayes_Fut2, Bayes_Fut3, Bayes_Fut4),
list(Bayes_Fut1, Bayes_Fut2, Bayes_Fut3, Bayes_Fut4))
# Combo
Bayes_SA_Sup1 <- matrix(nrow = 1, ncol = 3)
Bayes_SA_Sup1[1,] <- c(0.20, 0.95, 0.90)
# Mono
Bayes_SA_Sup2 <- matrix(nrow = 1, ncol = 3)
Bayes_SA_Sup2[1,] <- c(0.15, 0.80, 0.75)
# Backbone
Bayes_SA_Sup3 <- matrix(nrow = 1, ncol = 3)
Bayes_SA_Sup3[1,] <- c(0.15, 0.80, 0.75)
# Placebo
Bayes_SA_Sup4 <- matrix(nrow = 1, ncol = 3)
Bayes_SA_Sup4[1,] <- c(0.15, 0.80, 0.75)
Bayes_SA_Sup <- list(list(Bayes_SA_Sup1, Bayes_SA_Sup2, Bayes_SA_Sup3, Bayes_SA_Sup4),
list(Bayes_SA_Sup1, Bayes_SA_Sup2, Bayes_SA_Sup3, Bayes_SA_Sup4))
## Combo
Bayes_SA_Fut1 <- matrix(nrow = 1, ncol = 2)
Bayes_SA_Fut1[1,] <- c(0.20, 0.50)
# Mono
Bayes_SA_Fut2 <- matrix(nrow = 1, ncol = 2)
Bayes_SA_Fut2[1,] <- c(0.15, 0.50)
# Backbone
Bayes_SA_Fut3 <- matrix(nrow = 1, ncol = 2)
Bayes_SA_Fut3[1,] <- c(0.15, 0.50)
# Placebo
Bayes_SA_Fut4 <- matrix(nrow = 1, ncol = 2)
Bayes_SA_Fut4[1,] <- c(0.15, 0.50)
Bayes_SA_Fut <- list(list(Bayes_SA_Fut1, Bayes_SA_Fut2, Bayes_SA_Fut3, Bayes_SA_Fut4),
list(Bayes_SA_Fut1, Bayes_SA_Fut2, Bayes_SA_Fut3, Bayes_SA_Fut4))
# Comparison Combo vs Mono
P_Sup1 <- list(list(
testfun = function(x) stats::prop.test(x, alternative = "less", correct = FALSE),
p_sup = 0.025, p_prom = 0.10, p_adj = "B"))
# Comparison Combo vs Backbone
P_Sup2 <- list(list(
testfun = function(x) stats::prop.test(x, alternative = "less", correct = FALSE),
p_sup = 0.025, p_prom = 0.10, p_adj = "B"))
# Comparison Mono vs Placebo
P_Sup3 <- list(list(
testfun = function(x) stats::prop.test(x, alternative = "less", correct = FALSE),
p_sup = 0.050, p_prom = 0.10, p_adj = "B"))
P_Sup4 <- list(list(
testfun = function(x) stats::prop.test(x, alternative = "less", correct = FALSE),
p_sup = 0.050, p_prom = 0.10, p_adj = "B"))
P_Sup <- list(list(P_Sup1, P_Sup2, P_Sup3, P_Sup4),
list(P_Sup1, P_Sup2, P_Sup3, P_Sup4))
# Comparison Combo vs Mono
P_Fut1 <- list(list(
testfun = function(x) stats::prop.test(x, alternative = "less", correct = FALSE),
p_fut = 0.5, p_adj = "none"))
# Comparison Combo vs Backbone
P_Fut2 <- list(list(
testfun = function(x) stats::prop.test(x, alternative = "less", correct = FALSE),
p_fut = 0.5, p_adj = "none"))
# Comparison Mono vs Placebo
P_Fut3 <- list(list(
testfun = function(x) stats::prop.test(x, alternative = "less", correct = FALSE),
p_fut = 0.5, p_adj = "none"))
# Comparison Backbone Placebo
P_Fut4 <- list(list(
testfun = function(x) stats::prop.test(x, alternative = "less", correct = FALSE),
p_fut = 0.5, p_adj = "none"))
P_Fut <- list(list(P_Fut1, P_Fut2, P_Fut3, P_Fut4),
list(P_Fut1, P_Fut2, P_Fut3, P_Fut4))
# Comparison Combo vs Mono
Est_Sup_Fut1 <- list(list(est = "AR", p_hat_sup = 0.6, p_hat_fut = 0.1, p_hat_prom = 0.5))
# Comparison Combo vs Backbone
Est_Sup_Fut2 <- list(list(est = "RR", p_hat_sup = 1.25, p_hat_fut = 0.75, p_hat_prom = 1.5))
# Comparison Mono vs Placebo
Est_Sup_Fut3 <- list(list(est = "OR", p_hat_sup = 1.50, p_hat_fut = 0.75, p_hat_prom = 2))
Est_Sup_Fut4 <- list(list(est = "OR", p_hat_sup = 1.50, p_hat_fut = 0.75, p_hat_prom = 2))
Est_Sup_Fut <- list(list(Est_Sup_Fut1, Est_Sup_Fut2, Est_Sup_Fut3, Est_Sup_Fut4),
list(Est_Sup_Fut1, Est_Sup_Fut2, Est_Sup_Fut3, Est_Sup_Fut4))
# Comparison Combo vs Mono
CI_Sup_Fut1 <- list(list(est = "AR", ci = 0.95, p_hat_lower_sup = 0.35,
p_hat_upper_fut = 0.25, p_hat_lower_prom = 0.3))
# Comparison Combo vs Backbone
CI_Sup_Fut2 <- list(list(est = "RR", ci = 0.95, p_hat_lower_sup = 1.10,
p_hat_upper_fut = 1.10, p_hat_lower_prom = 1.05))
# Comparison Mono vs Placebo
CI_Sup_Fut3 <- list(list(est = "OR", ci = 0.95, p_hat_lower_sup = 1.20,
p_hat_upper_fut = 1.20, p_hat_lower_prom = 1.10))
CI_Sup_Fut4 <- list(list(est = "OR", ci = 0.95, p_hat_lower_sup = 1.20,
p_hat_upper_fut = 1.20, p_hat_lower_prom = 1.10))
CI_Sup_Fut <- list(list(CI_Sup_Fut1, CI_Sup_Fut2, CI_Sup_Fut3, CI_Sup_Fut4),
list(CI_Sup_Fut1, CI_Sup_Fut2, CI_Sup_Fut3, CI_Sup_Fut4))
make_decision_trial(res_list = res_list, which_cohort = which_cohort, interim = interim,
Bayes_Sup = Bayes_Sup, sharing_type = sharing_type,
Bayes_Fut = Bayes_Fut, Bayes_SA_Sup = Bayes_SA_Sup, Bayes_SA_Fut = Bayes_SA_Fut, P_Sup = P_Sup,
P_Fut = P_Fut, Est_Sup_Fut = Est_Sup_Fut, CI_Sup_Fut = CI_Sup_Fut
)
#> $Cohort1
#> $Cohort1$decision
#> [1] "STOP_FUT" "STOP_FUT"
#>
#> $Cohort1$alloc_ratio
#> [1] 1 1 1 1
#>
#> $Cohort1$n_thresh
#> [1] Inf 210
#>
#> $Cohort1$Comb
#> $Cohort1$Comb$rr
#> [1] 0.2
#>
#> $Cohort1$Comb$resp_bio
#> [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 1 0 1 0 1 0 1 0 1 1 0 0 1 0 0
#> [39] 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0
#>
#> $Cohort1$Comb$resp_hist
#> [1] 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0
#> [39] 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
#>
#> $Cohort1$Comb$n
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [39] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>
#>
#> $Cohort1$Mono
#> $Cohort1$Mono$rr
#> [1] 0.15
#>
#> $Cohort1$Mono$resp_bio
#> [1] 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 1 0
#> [39] 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0
#>
#> $Cohort1$Mono$resp_hist
#> [1] 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1
#> [39] 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>
#> $Cohort1$Mono$n
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [39] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>
#>
#> $Cohort1$Back
#> $Cohort1$Back$rr
#> [1] 0.15
#>
#> $Cohort1$Back$resp_bio
#> [1] 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [39] 0 0 0 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 0
#>
#> $Cohort1$Back$resp_hist
#> [1] 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0
#> [39] 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
#>
#> $Cohort1$Back$n
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [39] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>
#>
#> $Cohort1$Plac
#> $Cohort1$Plac$rr
#> [1] 0.1
#>
#> $Cohort1$Plac$resp_bio
#> [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
#> [39] 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>
#> $Cohort1$Plac$resp_hist
#> [1] 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [39] 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
#>
#> $Cohort1$Plac$n
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [39] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>
#>
#> $Cohort1$prob_sup1
#> [1] 0.8514739 0.8514739 0.9983249 0.9983249
#>
#> $Cohort1$prob_sup2
#> [1] 0.3303582 NA 0.7939001 0.7939001
#>
#> $Cohort1$prob_sup3
#> [1] 0.1196629 NA NA NA
#>
#> $Cohort1$prob_fut1
#> [1] NA NA 0.9983249 0.9983249
#>
#> $Cohort1$prob_sup_sa1
#> [1] 0.8167349257 0.7022938505 0.7022938505 0.0004197073
#>
#> $Cohort1$prob_fut_sa1
#> [1] 0.8167349257 0.7022938505 0.7022938505 0.0004197073
#>
#> $Cohort1$p_values_sup1
#> [1] 0.297071507 0.297071507 0.004844724 0.004844724
#>
#> $Cohort1$p_values_fut1
#> [1] 0.148535754 0.148535754 0.002422362 0.002422362
#>
#> $Cohort1$p_hat1
#> [1] 0.2428571 1.3263736 6.5590600 6.5590600
#>
#> $Cohort1$p_hat_upper1
#> [1] 0.2943198 2.5676839 47.4315121 47.4315121
#>
#> $Cohort1$p_hat_lower1
#> [1] 0.1181487 0.6851572 1.6703924 1.6703924
#>
#> $Cohort1$sup_interim_list
#> $Cohort1$sup_interim_list[[1]]
#> [,1] [,2] [,3] [,4]
#> [1,] FALSE FALSE TRUE TRUE
#> [2,] FALSE NA FALSE FALSE
#> [3,] FALSE NA NA NA
#>
#> $Cohort1$sup_interim_list[[2]]
#> [,1] [,2] [,3] [,4]
#> [1,] FALSE FALSE FALSE FALSE
#>
#> $Cohort1$sup_interim_list[[3]]
#> [,1] [,2] [,3] [,4]
#> [1,] FALSE FALSE TRUE TRUE
#>
#> $Cohort1$sup_interim_list[[4]]
#> [,1] [,2] [,3] [,4]
#> [1,] FALSE TRUE TRUE TRUE
#>
#> $Cohort1$sup_interim_list[[5]]
#> [,1] [,2] [,3] [,4]
#> [1,] FALSE FALSE TRUE TRUE
#>
#>
#> $Cohort1$fut_interim_list
#> $Cohort1$fut_interim_list[[1]]
#> [,1] [,2] [,3] [,4]
#> [1,] NA NA FALSE FALSE
#>
#> $Cohort1$fut_interim_list[[2]]
#> [,1] [,2] [,3] [,4]
#> [1,] FALSE FALSE FALSE TRUE
#>
#> $Cohort1$fut_interim_list[[3]]
#> [,1] [,2] [,3] [,4]
#> [1,] FALSE FALSE FALSE FALSE
#>
#> $Cohort1$fut_interim_list[[4]]
#> [,1] [,2] [,3] [,4]
#> [1,] FALSE FALSE FALSE FALSE
#>
#> $Cohort1$fut_interim_list[[5]]
#> [,1] [,2] [,3] [,4]
#> [1,] FALSE FALSE FALSE FALSE
#>
#>
#> $Cohort1$prom_interim_list
#> $Cohort1$prom_interim_list[[1]]
#> [,1] [,2] [,3] [,4]
#> [1,] FALSE FALSE FALSE FALSE
#> [2,] FALSE NA TRUE TRUE
#> [3,] FALSE NA NA NA
#>
#> $Cohort1$prom_interim_list[[2]]
#> [,1] [,2] [,3] [,4]
#> [1,] FALSE FALSE FALSE FALSE
#>
#> $Cohort1$prom_interim_list[[3]]
#> [,1] [,2] [,3] [,4]
#> [1,] FALSE FALSE TRUE TRUE
#>
#> $Cohort1$prom_interim_list[[4]]
#> [,1] [,2] [,3] [,4]
#> [1,] FALSE FALSE TRUE TRUE
#>
#> $Cohort1$prom_interim_list[[5]]
#> [,1] [,2] [,3] [,4]
#> [1,] FALSE FALSE TRUE TRUE
#>
#>
#> $Cohort1$sup_interim
#> [1] FALSE
#>
#> $Cohort1$fut_interim
#> [1] TRUE
#>
#> $Cohort1$prom_interim
#> [1] FALSE
#>
#>