stan_nbg  R Documentation 
Fit Neuenschwander, Branson & Gsponer logit model for dosefinding using Stan for full Bayesian inference.
stan_nbg(
outcome_str = NULL,
real_doses,
d_star,
target,
alpha_mean = NULL,
alpha_sd = NULL,
beta_mean = NULL,
beta_sd = NULL,
doses_given = NULL,
tox = NULL,
weights = NULL,
...
)
outcome_str 
A string representing the outcomes observed hitherto.
See 
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 dosefinding trial of doses 10mg, 20mg and 50mg, use c(10, 20, 50). 
d_star 
d_star, numeric reference doselevel. The linear covariate
in this logit model is 
target 
the target toxicity probability, a number between 0 and 1. 
alpha_mean 
Prior mean of intercept variable for normal prior. See Details. 
alpha_sd 
Prior standard deviation of intercept variable for normal prior. See Details. 
beta_mean 
Prior mean of gradient variable for normal prior. See Details. 
beta_sd 
Prior standard deviation of slope variable for normal prior. See Details. 
doses_given 
A optional vector of doselevels given to patients
1:num_patients, where 1=lowest dose, 2=second dose, etc. Only required when

tox 
An optional vector of toxicity outcomes for patients
1:num_patients, where 1=toxicity and 0=no toxicity. Only required when

weights 
An optional vector of numeric weights for the observations
for patients 1:num_patients, thus facilitating a timetoevent (TITE) design.
Can be used with 
... 
Extra parameters are passed to 
The quickest and easiest way to fit this model to some observed outcomes
is to describe the outcomes using trialr's syntax for dosefinding
outcomes. See df_parse_outcomes
for full details and examples.
The twoparameter model form is:
F(x_{i}, \alpha, \beta) = 1 / (1 + \exp{(\alpha + \exp{(\beta)} log(x_i / d_star))})
and the required parameters are:
alpha_mean
alpha_sd
beta_mean
beta_sd
An object of class nbg_fit
, which inherits behaviour from
crm_fit
.
Kristian Brock kristian.brock@gmail.com
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
crm_fit
sampling
## Not run:
# NonTITE example:
fit1 < stan_nbg('1NNN 2NNN 3TTT', real_doses = c(10, 20, 50, 100, 200),
d_star = 200, target = 0.25,
alpha_mean = 1, alpha_sd = 2,
beta_mean = 0, beta_sd = 1,
seed = 123)
fit1$recommended_dose
# The seed is passed to the Stan sampler. The usual Stan sampler params like
# cores, iter, chains etc are passed on too via the ellipsis operator.
# TITECRM example
fit2 <stan_nbg(real_doses = c(10, 20, 50, 100, 200), d_star = 200,
target = 0.25,
doses_given = c(3, 3, 3, 3),
tox = c(0, 0, 0, 0),
weights = c(73, 66, 35, 28) / 126,
alpha_mean = 1, alpha_sd = 2,
beta_mean = 0, beta_sd = 1,
seed = 123)
fit2$recommended_dose
## End(Not run)
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