Get an object to fit Wages & Tait's model for phase I/II dose-finding.

This function returns an object that can be used to fit Wages & Taits model for phase I/II dose-finding, i.e. it selects doses according to efficacy and toxicity outcomes. This function delegates prior-to-posterior calculations to the dfcrm package.

Usage

get_wages_and_tait(
  parent_selector_factory = NULL,
  tox_skeleton,
  eff_skeletons,
  eff_skeleton_weights = rep(1, nrow(eff_skeletons)),
  tox_limit,
  eff_limit,
  num_randomise,
  ...
)

Arguments

parent_selector_factory: optional object of type selector_factory that is in charge of dose selection before this class gets involved. Leave NULL to just use this model from the start.
tox_skeleton: Dose-toxicity skeleton, a non-decreasing vector of probabilities.
eff_skeletons: Matrix of dose-efficacy skeletons, with the skeletons in rows. I.e. number of cols is equal to number of doses, and number of rows is equal to number of efficacy skeletons under consideration.
eff_skeleton_weights: numerical vector, prior weights to efficacy skeletons. Should have length equal to number of rows in eff_skeletons. Default is equal weights.
tox_limit: We seek a dose with probability of toxicity no greater than this. Value determines the admissible set. See Wages & Tait (2015).
eff_limit: We seek a dose with probability of efficacy no less than this.
num_randomise: integer, maximum number of patients to use in the adaptive randomisation phase of the trial.
...: Extra args are passed onwards.

Value

an object of type selector_factory.

References

Wages, N. A., & Tait, C. (2015). Seamless Phase I/II Adaptive Design for Oncology Trials of Molecularly Targeted Agents. Journal of Biopharmaceutical Statistics, 25(5), 903–920. https://doi.org/10.1080/10543406.2014.920873

Examples

# Example in Wages & Tait (2015)
tox_skeleton = c(0.01, 0.08, 0.15, 0.22, 0.29, 0.36)
eff_skeletons = matrix(nrow=11, ncol=6)
eff_skeletons[1,] <- c(0.60, 0.50, 0.40, 0.30, 0.20, 0.10)
eff_skeletons[2,] <- c(0.50, 0.60, 0.50, 0.40, 0.30, 0.20)
eff_skeletons[3,] <- c(0.40, 0.50, 0.60, 0.50, 0.40, 0.30)
eff_skeletons[4,] <- c(0.30, 0.40, 0.50, 0.60, 0.50, 0.40)
eff_skeletons[5,] <- c(0.20, 0.30, 0.40, 0.50, 0.60, 0.50)
eff_skeletons[6,] <- c(0.10, 0.20, 0.30, 0.40, 0.50, 0.60)
eff_skeletons[7,] <- c(0.20, 0.30, 0.40, 0.50, 0.60, 0.60)
eff_skeletons[8,] <- c(0.30, 0.40, 0.50, 0.60, 0.60, 0.60)
eff_skeletons[9,] <- c(0.40, 0.50, 0.60, 0.60, 0.60, 0.60)
eff_skeletons[10,] <- c(0.50, 0.60, 0.60, 0.60, 0.60, 0.60)
eff_skeletons[11,] <- c(rep(0.60, 6))
eff_skeleton_weights = rep(1, nrow(eff_skeletons))
tox_limit = 0.33
eff_limit = 0.05
model <- get_wages_and_tait(tox_skeleton = tox_skeleton,
                            eff_skeletons = eff_skeletons,
                            tox_limit = tox_limit, eff_limit = eff_limit,
                            num_randomise = 20)
fit <- model %>% fit('1NN 2EN 3BE')
fit %>% recommended_dose()
#> [1] 1
fit %>% is_randomising()
#> [1] TRUE
fit %>% dose_admissible()
#> [1]  TRUE  TRUE  TRUE  TRUE FALSE FALSE
fit %>% prob_administer()
#> [1] 0.2017701 0.2500917 0.2980465 0.2500917 0.0000000 0.0000000