Fit a Neuenschwander, Branson & Gsponer logit dose-finding model
Fit Neuenschwander, Branson & Gsponer logit model for dose-finding 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 dose-finding trial of doses 10mg, 20mg and 50mg, use c(10, 20, 50). |
d_star |
d_star, numeric reference dose-level. 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 dose-levels 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 time-to-event (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 dose-finding
outcomes. See df_parse_outcomes for full details and examples.
The two-parameter model form is:
F(x_{i}, α, β) = 1 / (1 + \exp{-(α + \exp{(β)} 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
## Not run:
# Non-TITE 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$recommended_dose
# The seed is passed to the Stan sampler. The usual Stan sampler params like
# cores, iter, chains etc are passed on too via the ellipsis operator.
# TITE-CRM example
fit2 <-stan_nbg(real_doses = c(10, 20, 50, 100, 200), d_star = 200,
target = 0.25,
doses_given = c(3, 3, 3, 3),
tox = c(0, 0, 0, 0),
weights = c(73, 66, 35, 28) / 126,
alpha_mean = -1, alpha_sd = 2,
beta_mean = 0, beta_sd = 1,
seed = 123)
fit2$recommended_dose
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.