`make_decision_trial.Rd`

Given 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,
Bayes_Sup1 = NULL,
Bayes_Fut1 = NULL,
Bayes_Sup2 = NULL,
Bayes_Fut2 = NULL,
w = 0.5,
analysis_number,
beta_prior = 0.5,
hist_lag,
endpoint_number,
analysis_time,
dataset,
hist_miss = TRUE,
sharing_type,
...
)
```

- res_list
List item containing individual cohort trial results so far in a format used by the other functions in this package

- which_cohort
Current cohort that should be evaluated

- Bayes_Sup1
List of matrices with rows corresponding to number of multiple Bayesian posterior two-arm combination criteria for superiority of histology endpoint 1

- Bayes_Fut1
List of matrices with rows corresponding to number of multiple Bayesian posterior two-arm combination criteria for futility of histology endpoint 1

- Bayes_Sup2
List of matrices with rows corresponding to number of multiple Bayesian posterior two-arm combination criteria for superiority of histology endpoint 2

- Bayes_Fut2
List of matrices with rows corresponding to number of multiple Bayesian posterior two-arm combination criteria for futility of histology endpoint 2

- w
If dynamic borrowing, what is the prior choice for w. Default is 0.5.

- analysis_number
1st, second or third analysis?

- beta_prior
Prior parameter for all Beta Distributions. Default is 0.5.

- hist_lag
Histology Lag

- endpoint_number
Should histology endpoint 1 or 2 be evaluated?

- analysis_time
Platform Time of Analysis

- dataset
Dataset to be used for analysis

- hist_miss
Whether or not to exclude missing histology data

- sharing_type
Type of Data Sharing to perform

- ...
Further arguments inherited from simulate_trial

List containing original res_list and results of decision rules

```
# Example 1
# Initialize empty data frame
cols <- c("PatID", "ArrivalTime", "Cohort", "Arm", "RespHist1", "RespHist2", "HistMissing")
df <- matrix(nrow = 100, ncol = length(cols))
colnames(df) <- cols
df <- as.data.frame(df)
df$PatID <- 1:100
df$ArrivalTime <- sort(runif(100, min = 0, max = 5))
df$Cohort <- sample(1:2, 100, replace = TRUE)
df$Arm <- sample(c("Combo", "Plac"), 100, replace = TRUE)
df$RespHist1 <- sample(0:1, 100, replace = TRUE)
df$RespHist2 <- sample(0:1, 100, replace = TRUE)
df$HistMissing <- sample(0:1, 100, replace = TRUE, prob = c(0.95, 0.05))
# Initialize res_list Object
res_list <-
rep(
list(
list(
Meta = list(
decision = rep("none", 3),
decision_hist1 = rep("none", 3),
decision_hist2 = rep("none", 3),
start_n = 0,
start_time = 0,
pat_enrolled = 0
),
Arms = rep(
list(
list(
rr = NULL,
hist_observed = 0
)
),
2
)
)
),
2
)
arm_names <- c("Comb", "Plac")
for (i in 1:2) {
names(res_list)[i] <- paste0("Cohort", i)
names(res_list[[i]]$Arms) <- arm_names
res_list[[i]]$Arms$Comb$rr <- matrix(c(0.2, 0.2), ncol = 2)
res_list[[i]]$Arms$Plac$rr <- matrix(c(0.1, 0.1), ncol = 2)
}
sharing_type <- "all"
analysis_number <- 3
which_cohort <- 1
endpoint_number <- 2
hist_lag <- 1
analysis_time <- 6
# Comparison IA1
Bayes_Sup11 <- matrix(nrow = 2, ncol = 2)
Bayes_Sup11[1,] <- c(0.00, 0.95)
Bayes_Sup11[2,] <- c(0.10, 0.80)
# Comparison IA2
Bayes_Sup12 <- matrix(nrow = 2, ncol = 2)
Bayes_Sup12[1,] <- c(0.00, 0.95)
Bayes_Sup12[2,] <- c(NA, NA)
# Comparison IA3
Bayes_Sup13 <- matrix(nrow = 2, ncol = 2)
Bayes_Sup13[1,] <- c(0.00, 0.95)
Bayes_Sup13[2,] <- c(0.10, 0.80)
Bayes_Sup1 <- Bayes_Sup2 <- list(list(Bayes_Sup11), list(Bayes_Sup12), list(Bayes_Sup13))
# DO NOT RUN
res_list2 <-
make_decision_trial(
res_list = res_list, which_cohort = which_cohort,
analysis_number = analysis_number, endpoint_number = endpoint_number,
Bayes_Sup1 = Bayes_Sup1, Bayes_Sup2 = Bayes_Sup2,
dataset = df, analysis_time = analysis_time, hist_lag = hist_lag,
sharing_type = sharing_type
)
```