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.
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, ... )
parent_selector_factory |
optional object of type
|
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
|
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. |
an object of type selector_factory.
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
# 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()
fit %>% is_randomising()
fit %>% dose_admissible()
fit %>% prob_administer()Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.