Along with selector, this is the second core class in the escalation package. It exists to do one thing: fit outcomes from dose-finding trials to the models we use to select doses.

A selector_factory object is obtained by initially calling a function like get_dfcrm, get_three_plus_three or get_boin. Users may then add desired extra behaviour with subsequent calls to functions like stop_when_n_at_dose or stop_when_too_toxic. selector objects are obtained by calling the fit function on a selector_factory object. Refer to examples to see how this works.

selector_factory()

See also

Examples

# 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()
#> [1] 1
fit1 %>% continue()
#> [1] TRUE
fit2 %>% recommended_dose()
#> [1] 1
fit2 %>% continue()
#> [1] TRUE
fit3 %>% recommended_dose()
#> [1] NA
fit3 %>% continue()
#> [1] FALSE
# 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()
#> [1] 1
fit1 %>% continue()
#> [1] TRUE
fit2 %>% recommended_dose()
#> [1] 1
fit2 %>% continue()
#> [1] FALSE
# 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()
#> [1] 2
fit4 %>% continue()
#> [1] TRUE