stop_when_too_toxic: Stop trial and recommend no dose when a dose is too toxic.

In escalation: A Modular Approach to Dose-Finding Clinical Trials

Stop trial and recommend no dose when a dose is too toxic.

Description

This method stops a dose-finding trial and recommends no dose 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.

Usage

stop_when_too_toxic(parent_selector_factory, dose, tox_threshold, confidence)

Arguments

parent_selector_factory	Object of type selector_factory.
dose	'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.
tox_threshold	We are interested in toxicity probabilities greater than this threshold.
confidence	Stop when there is this much total probability mass supporting that the toxicity rate exceeds the threshold.

Details

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.

Value

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

Examples

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()
fit1 %>% continue()

# However, after the material toxicity at dose 3, ithe rule is fired:
fit2 %>% recommended_dose()
fit2 %>% continue()
# To verify the requirement to stop, let's calculate the probability that the
# toxicity rate exceeds 50%
fit2 %>% prob_tox_exceeds(0.5)

escalation documentation built on June 27, 2024, 5:09 p.m.