Select dose by mTPI2's MTD-choosing algorithm. — select_mtpi2

Note: if you use this selector, it almost certainly needs to be the last example in the chain - see Example below. This method selects dose by the algorithm for identifying the maximum tolerable dose (MTD) described in Guo et al. (2017). This class is intended to be used when a mTPI2 trial has reached its maximum sample size. Thus, it intends to make the final dose recommendation after the regular mTPI2 dose selection algorithm, as implemented by get_mtpi2, 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_mtpi2_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

Guo, W., Wang, SJ., Yang, S., Lynn, H., Ji, Y. (2017). A Bayesian Interval Dose-Finding Design Addressing Ockham's Razor: mTPI-2. https://doi.org/10.1016/j.cct.2017.04.006

Examples

# This class is intended to make the final dose selection in a mTPI2 trial:
target <- 0.25
model <- get_mtpi2(num_doses = 5, target = target,
                   epsilon1 = 0.05, epsilon2 = 0.05,
                   exclusion_certainty = 0.95) %>%
  stop_at_n(n = 12) %>%
  select_mtpi2_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_mtpi2(num_doses = 5, target = target,
                    epsilon1 = 0.05, epsilon2 = 0.05,
                    exclusion_certainty = 0.95) %>%
  select_mtpi2_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_mtpi2_mtd(when = 'always', exclusion_certainty = 0.95)
model3 %>% fit('1NNT') %>% recommended_dose()
#> [1] 1
model3 %>% fit('1NNN 2NNT') %>% recommended_dose()
#> [1] 1