This function returns an object that can be used to fit the EffTox model for phase I/II dose-finding using methods provided by the trialr package.
get_trialr_efftox( parent_selector_factory = NULL, real_doses, efficacy_hurdle, toxicity_hurdle, p_e, p_t, eff0, tox1, eff_star, tox_star, priors, ... )
optional object of type
A vector of numbers, the doses under investigation. They should be ordered from lowest to highest and be in consistent units. E.g. to conduct a dose-finding trial of doses 10mg, 20mg and 50mg, use c(10, 20, 50).
Minimum acceptable efficacy probability. A number between 0 and 1.
Maximum acceptable toxicity probability. A number between 0 and 1.
Certainty required to infer a dose is acceptable with regards to being probably efficacious; a number between 0 and 1.
Certainty required to infer a dose is acceptable with regards to being probably tolerable; a number between 0 and 1.
Efficacy probability required when toxicity is impossible; a number between 0 and 1 (see Details).
Toxicity probability permitted when efficacy is guaranteed; a number between 0 and 1 (see Details).
Efficacy probability of an equi-utility third point (see Details).
Toxicity probability of an equi-utility third point (see Details).
instance of class
Extra args are passed to
an object of type
selector_factory that can fit the
EffTox model to outcomes.
Thall, P., & Cook, J. (2004). Dose-Finding Based on Efficacy-Toxicity Trade-Offs. Biometrics, 60(3), 684-693. https://doi.org/10.1111/j.0006-341X.2004.00218.x
Thall, P., Herrick, R., Nguyen, H., Venier, J., & Norris, J. (2014). Effective sample size for computing prior hyperparameters in Bayesian phase I-II dose-finding. Clinical Trials, 11(6), 657-666. https://doi.org/10.1177/1740774514547397
Brock, K. (2020). trialr: Clinical Trial Designs in 'rstan'. R package version 0.1.5. https://github.com/brockk/trialr
Brock, K. (2019). trialr: Bayesian Clinical Trial Designs in R and Stan. arXiv preprint arXiv:1907.00161.
efftox_priors <- trialr::efftox_priors p <- efftox_priors(alpha_mean = -7.9593, alpha_sd = 3.5487, beta_mean = 1.5482, beta_sd = 3.5018, gamma_mean = 0.7367, gamma_sd = 2.5423, zeta_mean = 3.4181, zeta_sd = 2.4406, eta_mean = 0, eta_sd = 0.2, psi_mean = 0, psi_sd = 1) real_doses = c(1.0, 2.0, 4.0, 6.6, 10.0) model <- get_trialr_efftox(real_doses = real_doses, efficacy_hurdle = 0.5, toxicity_hurdle = 0.3, p_e = 0.1, p_t = 0.1, eff0 = 0.5, tox1 = 0.65, eff_star = 0.7, tox_star = 0.25, priors = p, iter = 1000, chains = 1, seed = 2020)