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.

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] 4
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