This method stops a dose-finding trial when sufficient probabilistic confidence is reached that the rate of toxicity at a dose exceeds some threshold. In other words, it stops when it is likely that a dose is too toxic. It can stop when the rule is triggered at the recommended dose, at a particular dose, or at any dose. See Details.

stop_when_too_toxic(parent_selector_factory, dose, tox_threshold, confidence)



Object of type selector_factory.


'any' to stop when any dose is too toxic; 'recommended' to stop when the recommended dose is too toxic; or an integer to stop when a particular dose-level is too toxic.


We are interested in toxicity probabilities greater than this threshold.


Stop when there is this much total probability mass supporting that the toxicity rate exceeds the threshold.


an object of type selector_factory that can fit a dose-finding model to outcomes.


The method for calculating probability mass for toxicity rates will ultimately be determined by the dose-finding model used and the attendant inferential mechanism. For instance, the crm function in the dfcrm package calculates the posterior expected mean and variance of the slope parameter in a CRM model. It does not use MCMC to draw samples from the posterior distribution. Thus, to perform inference on the posterior probability of toxicity, this package assumes the dfcrm slope parameter follows a normal distribution with the mean and variance calculated by dfcrm. In contrast, the stan_crm function in the trialr package needs no such assumption because it samples from the posterior parameter distribution and uses those samples to infer on the posterior probability of toxicity at each dose, dependent on the chosen model for the dose-toxicity curve.


skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6) target <- 0.25 # We compare a CRM model without a toxicity stopping rule to one with it: model1 <- get_dfcrm(skeleton = skeleton, target = target) model2 <- get_dfcrm(skeleton = skeleton, target = target) %>% stop_when_too_toxic(dose = 'any', tox_threshold = 0.5, confidence = 0.7) outcomes <- '1NNN 2NNN 3NNT 3NNN 3TNT 2NNN' fit1 <- model1 %>% fit(outcomes) fit2 <- model2 %>% fit(outcomes) # Naturally the first does not advocate stopping: fit1 %>% recommended_dose()
#> [1] 3
fit1 %>% continue()
#> [1] TRUE
# However, after the material toxicity at dose 3, ithe rule is fired: fit2 %>% recommended_dose()
#> [1] 3
fit2 %>% continue()
#> [1] FALSE
# To verify the requirement to stop, let's calculate the probability that the # toxicity rate exceeds 50% fit2 %>% prob_tox_exceeds(0.5)
#> [1] 4.393912e-07 2.464851e-05 8.098000e-03 1.458586e-01 8.025805e-01