get_trialr_efftox: Get an object to fit the EffTox model using the trialr...

In escalation: Modular Approach to Dose Finding Clinical Trials

Get an object to fit the EffTox model using the trialr package.

Description

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.

Usage

get_trialr_efftox(
  parent_selector_factory = NULL,
  real_doses,
  efficacy_hurdle,
  toxicity_hurdle,
  p_e,
  p_t,
  eff0,
  tox1,
  eff_star,
  tox_star,
  priors,
  ...
)

Arguments

parent_selector_factory	optional object of type selector_factory that is in charge of dose selection before this class gets involved. Leave as NULL to just use EffTox from the start.
real_doses	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).
efficacy_hurdle	Minimum acceptable efficacy probability. A number between 0 and 1.
toxicity_hurdle	Maximum acceptable toxicity probability. A number between 0 and 1.
p_e	Certainty required to infer a dose is acceptable with regards to being probably efficacious; a number between 0 and 1.
p_t	Certainty required to infer a dose is acceptable with regards to being probably tolerable; a number between 0 and 1.
eff0	Efficacy probability required when toxicity is impossible; a number between 0 and 1 (see Details).
tox1	Toxicity probability permitted when efficacy is guaranteed; a number between 0 and 1 (see Details).
eff_star	Efficacy probability of an equi-utility third point (see Details).
tox_star	Toxicity probability of an equi-utility third point (see Details).
priors	instance of class trialr{efftox_priors}, the hyperparameters for normal priors on the six model parameters.
...	Extra args are passed to stan_efftox.

Value

an object of type selector_factory that can fit the EffTox model to outcomes.

References

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.

Examples

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)

escalation documentation built on May 31, 2023, 6:32 p.m.