This method selects dose by the algorithm for identifying the optimal
biological dose (OBD) described in Lin et al. (2020). This class is intended
to be used when a BOIN12 trial has reached its maximum sample size. Thus, it
intends to make the final dose recommendation after the regular BOIN12 dose
selection algorithm, as implemented by get_boin12, has
gracefully concluded a dose-finding trial. However, the class can be used in
any scenario where there is a limit 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_boin12_obd(
parent_selector_factory,
when = c("finally", "always"),
tox_limit = NULL,
...
)Object of type selector_factory.
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'.
We seek a dose with toxicity probability no greater than. If not provided, the value will be sought from the parent dose-selector.
Extra args are ignored.
an object of type selector_factory.
Lin, R., Zhou, Y., Yan, F., Li, D., & Yuan, Y. (2020). BOIN12: Bayesian optimal interval phase I/II trial design for utility-based dose finding in immunotherapy and targeted therapies. JCO precision oncology, 4, 1393-1402.
# This class is intended to make the final dose selection in a BOIN12 trial:
tox_limit <- 0.35
model <- get_boin12(num_doses = 5, phi_t = 0.35, phi_e = 0.25,
u2 = 40, u3 = 60, n_star = 6) %>%
stop_at_n(n = 12) %>%
select_boin12_obd()
outcomes <- '1NNN 2NTN 2NNN 3NTT'
model %>% fit(outcomes) %>% recommended_dose()
#> [1] 1
# However, since behaviour is modular in this package, we can use this method
# to select dose at every dose decision:
model2 <- get_boin12(num_doses = 5, phi_t = 0.35, phi_e = 0.25,
u2 = 40, u3 = 60, n_star = 6) %>%
select_boin12_obd(when = 'always')
model2 %>% fit('1NNT') %>% recommended_dose()
#> [1] 1
model2 %>% fit('1NNN 2NNT') %>% recommended_dose()
#> [1] 1