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 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.
Usage
select_tpi_mtd(
parent_selector_factory,
when = c("finally", "always"),
target = NULL,
exclusion_certainty,
alpha = 1,
beta = 1,
pava_just_tested_doses = FALSE,
...
)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.
- pava_just_tested_doses
the design uses the PAVA method to estimate monotonic Prob(Tox) at the doses. By default, this estimates Prob(Tox) for doses that have not been tested in the trial and can lead to untested doses being recommended. Set this option to TRUE to use PAVA only on tested doses, leaving untested doses as NA and not-recommendable. Set to FALSE to use PAVA at all doses and potentially recommend an untested dose. Default is FALSE.
- ...
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