Select dose by TPI's MTD-choosing algorithm. — select_tpi

This method selects dose by the algorithm for identifying the maximum tolerable dose (MTD) described in Ji et al. (2007). This class is intended to be used when a TPI trial has reached its maximum sample size. Thus, it intends to make the final dose recommendation after the regular TPI dose selection algorithm, as implemented by get_tpi, including any additional behaviours that govern stopping (etc), has gracefully concluded a dose-finding trial. However, the class can be used in any scenario where there is a target toxicity rate. See Examples. Note - this class will not override the parent dose selector when the parent is advocating no dose. Thus this class will not reinstate a dangerous dose.

select_tpi_mtd(
  parent_selector_factory,
  when = c("finally", "always"),
  target = NULL,
  exclusion_certainty,
  alpha = 1,
  beta = 1,
  ...
)

Arguments

parent_selector_factory: Object of type selector_factory.
when: Either of: 'finally' to select dose only when the parent dose-selector has finished, by returning continue() == FALSE; or 'always' to use this dose-selection algorithm for every dose decision. As per the authors' original intentions, the default is 'finally'.
target: We seek a dose with this probability of toxicity. If not provided, the value will be sought from the parent dose-selector.
exclusion_certainty: Numeric, threshold posterior certainty required to exclude a dose for being excessively toxic. The authors discuss values in the range 0.7 - 0.95. Set to a value > 1 to suppress the dose exclusion mechanism. The authors use the Greek letter xi for this parameter.
alpha: First shape parameter of the beta prior distribution on the probability of toxicity.
beta: Second shape parameter of the beta prior distribution on the probability of toxicity.
...: Extra args are passed onwards.

Value

an object of type selector_factory.

References

Ji, Y., Li, Y., & Bekele, B. N. (2007). Dose-finding in phase I clinical trials based on toxicity probability intervals. Clinical Trials, 4(3), 235–244. https://doi.org/10.1177/1740774507079442

Examples

# This class is intended to make the final dose selection in a mTPI2 trial:
target <- 0.25
model <- get_tpi(num_doses = 5, target = target,
                 k1 = 1, k2 = 1.5,
                 exclusion_certainty = 0.95) %>%
  stop_at_n(n = 12) %>%
  select_tpi_mtd(exclusion_certainty = 0.95)

outcomes <- '1NNN 2NTN 2NNN 3NTT'
model %>% fit(outcomes) %>% recommended_dose()
#> [1] 2

# However, since behaviour is modular in this package, we can use this method
# to select dose at every dose decision if we wanted:
model2 <- get_tpi(num_doses = 5, target = target,
                  k1 = 1, k2 = 1.5,
                  exclusion_certainty = 0.95) %>%
  select_tpi_mtd(when = 'always', exclusion_certainty = 0.95)
model2 %>% fit('1NNT') %>% recommended_dose()
#> [1] 1
model2 %>% fit('1NNN 2NNT') %>% recommended_dose()
#> [1] 1

# and with any underlying model:
skeleton <- c(0.05, 0.1, 0.25, 0.4, 0.6)
model3 <- get_dfcrm(skeleton = skeleton, target = target) %>%
  select_tpi_mtd(when = 'always', exclusion_certainty = 0.95)
model3 %>% fit('1NNT') %>% recommended_dose()
#> [1] 1
model3 %>% fit('1NNN 2NNT') %>% recommended_dose()
#> [1] 1