trialr (version 0.1.6)

stan_nbg: Fit a Neuenschwander, Branson & Gsponer logit dose-finding model

Description

Fit Neuenschwander, Branson & Gsponer logit model for dose-finding using Stan for full Bayesian inference.

Usage

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,
  ...
)

Value

An object of class nbg_fit, which inherits behaviour from crm_fit.

Arguments

outcome_str

A string representing the outcomes observed hitherto. See df_parse_outcomes for a description of syntax and examples. Alternatively, you may provide doses_given and tox parameters. See Details.

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 dose / d_star.

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 outcome_str is not provided.

tox

An optional vector of toxicity outcomes for patients 1:num_patients, where 1=toxicity and 0=no toxicity. Only required when outcome_str is not provided.

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 outcome_str, or with doses_given and tox. It is generally tidier to specify doses_given, tox and weights when a TITE-analysis is desired.

...

Extra parameters are passed to rstan::sampling. Commonly used options are iter, chains, warmup, cores, and control.

Author

Kristian Brock

Details

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:

\(F(x_{i}, \alpha, \beta) = 1 / (1 + \exp{-(\alpha + \exp{(\beta)} log(x_i / d_star))}) \)

and the required parameters are:

• alpha_mean

• alpha_sd

• beta_mean

• beta_sd

References

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

See Also

crm_fit sampling

Examples

if (FALSE) {
# 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
}