Get the prior beliefs for a CRM trial scenario.
Infer the prior beliefs consistent with the parameters and model
form for a CRM dose-finding trial. This function could be interpreted as
fitting the model to no data, thus examining the beliefs on dose-toxicity
that are suggested by the parameter priors alone. This function provides the
task analagous to stan_crm before any data has been collected.
crm_prior_beliefs(
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,
...
)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 |
model |
Character string to denote desired model. One of |
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. |
... |
extra parameters passed to |
Different model choices require that different parameters are provided. See below.
An object of class crm_fit
empiric modelbeta_sd
logistic modela0
beta_mean
beta_sd
logistic_gamma modela0
beta_shape
beta_inverse_scale
logistic2 modelalpha_mean
alpha_sd
beta_mean
beta_sd
Kristian Brock
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://www.jstor.org/stable/2531628
Cheung, Y.K. (2011). Dose Finding by the Continual Reassessment Method. CRC Press. ISBN 9781420091519
skeleton <- c(0.05, 0.1, 0.15, 0.33, 0.5)
target <- 0.33
prior_fit1 <- crm_prior_beliefs(skeleton, target, model = 'empiric',
beta_sd = sqrt(1.34))
prior_fit2 <- crm_prior_beliefs(skeleton, target, model = 'logistic_gamma',
a0 = 3, beta_shape = 1,
beta_inverse_scale = 2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.