trialr (version 0.0.7)

stan_crm: Fit a CRM model

Description

Fit a CRM model using Stan for full Bayesian inference.

Usage

stan_crm(outcome_str = NULL, skeleton, target, model = c("empiric",
  "logistic", "logistic_gamma", "logistic2"), a0 = NULL, alpha_mean = NULL,
  alpha_sd = NULL, beta_mean = NULL, beta_sd = NULL, beta_shape = NULL,
  beta_inverse_scale = NULL, doses_given = NULL, tox = NULL, ...)

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.

skeleton

a vector of the prior guesses of toxicity at doses. This should be a monotonically-increasing vector of numbers between 0 and 1.

target

the target toxicity probability, a number between 0 and 1. This value would normally be one of the values in skeleton, but that is not a requirement.

model

Character string to denote desired model. One of empiric, logistic, logistic_gamma, or logistic2. The choice of model determines which parameters are required. See Details.

a0

Value of fixed intercept parameter. Only required for certain models. See Details.

alpha_mean

Prior mean of intercept variable for normal prior. Only required for certain models. See Details.

alpha_sd

Prior standard deviation of intercept variable for normal prior. Only required for certain models. See Details.

beta_mean

Prior mean of gradient variable for normal prior. Only required for certain models. See Details.

beta_sd

Prior standard deviation of slope variable for normal prior. Only required for certain models. See Details.

beta_shape

Prior shape parameter of slope variable for gamma prior. Only required for certain models. See Details.

beta_inverse_scale

Prior inverse scale parameter of slope variable for gamma prior. Only required for certain models. 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.

...

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

Value

An object of class crm_fit

Requirements of <code>empiric</code> model

* beta_sd

Requirements of <code>logistic</code> model

* a0 * beta_mean * beta_sd

Requirements of <code>logistic_gamma</code> model

* a0 * beta_shape * beta_inverse_scale

Requirements of <code>logistics</code> model

* a0 * alpha_mean * alpha_sd * beta_mean * beta_sd

Details

The quickest and easiest way to fit a CRM 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.

Different model parameterisations require that difference parameter values are specified.

References

O'Quigley, J., Pepe, M., & Fisher, L. (1990). Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics, 46(1), 33-48. https://doi.org/10.2307/2531628

See Also

crm_fit crm_process

Examples

# NOT RUN {
# This model is presented in Thall et al. (2014)
mod1 <- stan_crm('1N 2N 3T', skeleton = c(0.1, 0.2, 0.35, 0.6),
                 target = 0.2, model = 'empiric', beta_sd = sqrt(1.34),
                 seed = 123)

# Shorthand for the above is:
mod2 <- stan_efftox_demo('1N 2E 3B', seed = 123)

# 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.
# }