get_trialr_nbg.Rd
This function returns an object that can be used to fit a Neuenschwander, Branson and Gsponer (NBG) model for dosefinding using methods provided by the trialr package.
get_trialr_nbg( parent_selector_factory = NULL, real_doses, d_star, target, alpha_mean, alpha_sd, beta_mean, beta_sd, ... )
parent_selector_factory  optional object of type


real_doses  Doses under investigation, a nondecreasing vector of numbers. 
d_star  Numeric, reference dose for calculating the covariate

target  We seek a dose with this probability of toxicity. 
alpha_mean  Prior mean of intercept variable for normal prior. See Details. Also see documentation for trialr package for further details. 
alpha_sd  Prior standard deviation of intercept variable for normal prior. See Details. Also see documentation for trialr package for further details. 
beta_mean  Prior mean of gradient variable for normal prior. See Details. Also see documentation for trialr package for further details. 
beta_sd  Prior standard deviation of slope variable for normal prior. See Details. Also see documentation for trialr package for further details. 
...  Extra args are passed to 
an object of type selector_factory
that can fit the
NBG model to outcomes.
The model form implemented in trialr is:
\(F(x_{i}, \alpha, \beta) = 1 / (1 + \exp{(\alpha + \exp{(\beta)} log(x_i / d_*))}) \)
with normal priors on alpha and beta.
Dose selectors are designed to be daisychained together to achieve different
behaviours. This class is a **resumptive** selector, meaning it carries on
when the previous dose selector, where present, has elected not to continue.
For example, this allows instances of this class to be preceded by a selector
that follows a fixed path in an initial escalation plan, such as that
provided by follow_path
. In this example, when the observed
trial outcomes deviate from that initial plan, the selector following the
fixed path elects not to continue and responsibility passes to this class.
See examples under get_dfcrm
.
Neuenschwander, B., Branson, M., & Gsponer, T. (2008). Critical aspects of the Bayesian approach to phase I cancer trials. Statistics in Medicine, 27, 2420–2439. https://doi.org/10.1002/sim.3230
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.
real_doses < c(5, 10, 25, 40, 60) d_star < 60 target < 0.25 model < get_trialr_nbg(real_doses = real_doses, d_star = d_star, target = target, alpha_mean = 2, alpha_sd = 1, beta_mean = 0.5, beta_sd = 1) # Refer to the trialr documentation for more details on model & priors. outcomes < '1NNN 2NTN' fit < model %>% fit(outcomes) fit %>% recommended_dose()#> [1] 2#> [1] 0.1218123 0.2504148 0.5658892 0.7301690 0.8297591