Get an object to fit the BOIN12 model for phase I/II dose-finding.

This function returns an object that can be used to fit the BOIN12 model for phase I/II dose-finding, i.e. it selects doses according to efficacy and toxicity outcomes.

get_boin12(
  num_doses,
  phi_t,
  phi_e,
  u1 = 100,
  u2,
  u3,
  u4 = 0,
  n_star = 6,
  c_t = 0.95,
  c_e = 0.9,
  start_dose = 1,
  prior_alpha = 1,
  prior_beta = 1,
  ...
)

Arguments

num_doses

integer, num of doses under investigation

phi_t

Probability of toxicity threshold

phi_e

Probability of efficacy threshold

u1

utility of efficacy without toxicity, 100 by default

u2

utility of no efficacy and no toxicity, between u1 and u4

u3

utility of efficacy and toxicity, between u1 and u4

u4

utility of toxicity without efficacy , 0 by default

n_star

when tox is within bounds, stop exploring higher doses when n at dose is greater than or equal to this value. 6 by default.

c_t

certainty required to flag excess toxicity, 0.95 by default

c_e

certainty required to flag deficient efficacy, 0.9 by default

start_dose

index of starting dose, 1 by default (i.e. lowest dose)

prior_alpha

first shape param for prior on beta prior, 1 by default

prior_beta

second shape param for prior on beta prior, 1 by default

...

Extra args are passed onwards.

Value

an object of type selector_factory that can fit the BOIN12 model to outcomes.

References

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.

Examples

# Examples in Lin et al.
model <- get_boin12(num_doses = 5, phi_t = 0.35, phi_e = 0.25,
                    u2 = 40, u3 = 60, n_star = 6)
fit <- model %>% fit('1NNN 2ENT 3ETT 2EEN')
fit %>% recommended_dose()
#> [1] 2
fit %>% continue()
#> [1] TRUE
fit %>% is_randomising()
#> [1] FALSE
fit %>% dose_admissible()
#> [1]  TRUE  TRUE  TRUE FALSE FALSE
fit %>% prob_administer()
#>    1    2    3    4    5 
#> 0.25 0.50 0.25 0.00 0.00