Tabulate rank-based desirability scores for a BOIN12 trial

Tabulate rank-based desirability scores for a BOIN12 trial

boin12_rds(
  sample_sizes,
  phi_t,
  phi_e,
  u1 = 100,
  u2,
  u3,
  u4 = 0,
  c_t = 0.95,
  c_e = 0.9,
  prior_alpha = 1,
  prior_beta = 1
)

Arguments

sample_sizes

integer vector, cohort sample sizes to consider

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

c_t

certainty required to flag excess toxicity, 0.95 by default

c_e

certainty required to flag deficient efficacy, 0.9 by default

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

Value

data.frame with columns Patients, Toxicity, Efficacy containing the numbers of patients, patients with toxicitiy, and patients with efficacy; Admissble, containing the character labels Admissble or Not Admissible; RDS, containing a character label of the numerical desirability score or the character string "E", where a combination is eliminated; and RDS_x, containing the desirability scores as numbers, with NA where a combination should be eliminated.

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.

Author

Bharat Bhushan, Kristian Brock

Examples

# Table 3 in Lin et al.
x <- boin12_rds(
  sample_sizes = c(0, 3, 6, 9),
  phi_t = 0.35,
  phi_e = 0.25,
  u1 = 100,
  u2 = 40,
  u3 = 60,
  u4 = 0,
  c_t = 0.95,
  c_e = 0.9,
  prior_alpha = 1,
  prior_beta = 1
)