selector_factory()

# Start with a simple CRM model
skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
target <- 0.25
model1 <- get_dfcrm(skeleton = skeleton, target = target)

# Add a rule to stop when 9 patients are treated at the recommended dose
model2 <- get_dfcrm(skeleton = skeleton, target = target) %>%
  stop_when_n_at_dose(n = 9, dose = 'recommended')

# Add a rule to stop if toxicity rate at lowest dose likely exceeds target
model3 <- get_dfcrm(skeleton = skeleton, target = target) %>%
  stop_when_n_at_dose(n = 9, dose = 'recommended') %>%
  stop_when_too_toxic(dose = 1, tox_threshold = target, confidence = 0.5)

# We now have three CRM models that differ in their stopping behaviour.
# Let's fit each to some outcomes to see those differences:

outcomes <- '1NNN 2NTT 1NNT'
fit1 <- model1 %>% fit(outcomes)
fit2 <- model2 %>% fit(outcomes)
fit3 <- model3 %>% fit(outcomes)

fit1 %>% recommended_dose()
fit1 %>% continue()

fit2 %>% recommended_dose()
fit2 %>% continue()

fit3 %>% recommended_dose()
fit3 %>% continue()
# Already model3 wants to stop because of excessive toxicity.

# Let's carry on with models 1 and 2 by adding another cohort:

outcomes <- '1NNN 2NTT 1NNT 1NNN'
fit1 <- model1 %>% fit(outcomes)
fit2 <- model2 %>% fit(outcomes)

fit1 %>% recommended_dose()
fit1 %>% continue()

fit2 %>% recommended_dose()
fit2 %>% continue()

# Model1 wants to continue - in fact it will never stop.
# In contrast, model2 has seen 9 at dose 1 so, rather than suggest dose 1
# again, it suggests the trial should stop.

# For contrast, let us consider a BOIN model on the same outcomes
boin_fitter <- get_boin(num_doses = length(skeleton), target = target)
fit4 <- boin_fitter %>% fit(outcomes)
fit4 %>% recommended_dose()
fit4 %>% continue()