# 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. ```r 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. ## Returns an object of type `selector_factory`. ## Examples ```r # 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() ``` ## 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

get_wages_and_tait function