crm12Comb (version 0.1.12)

priorSkeletons: Generate the skeletons of toxicity and efficacy

Description

This function is used to generate skeletons of toxicity and efficacy. This is a modifed version based on getprior, which keep the same procedure using empiric and one-parameter logistic models assumed normal priors with \(mean=0\) and further add multiple models with various prior distributions including hyperbolic tangent model with exponential prior, empiric/one-parameter logistic models with normal prior and self-input mean values as well as with gamma prior, and two-parameter logistic model with normal/gamma priors.

Usage

priorSkeletons(updelta, target, npos, ndose,
               model = "empiric", prior = "normal",
               alpha_mean=NULL, beta_mean=0, a0 = 3,
               alpha_shape=NULL, alpha_inverse_scale=NULL,
               beta_shape=NULL, beta_inverse_scale=NULL)

Value

A vector of length \(ndose\) is returned.

Arguments

updelta

The half-width of the indifference intervals.

target

The target DLT rate.

npos

The prior guess of the position of MTD.

ndose

The number of testing doses.

model

A character string to specify the model used. The default model is "empiric". Other models include hyperbolic tangent model specified by "tanh", one-parameter logistic model specified by "logistic", and two-parameter logistic model specified by "logistic2".

prior

A character sting to specify the prior distribution of parameter. The default prior is "normal" used together with the model="empiric". Other prior distributions include "exponential" when model="tanh", "gamma" when model="empiric", "normal" and "gamma" when model="logistic" and "logistic2".

alpha_mean

The mean of intercept parameter of two-parameter logistic model only used when model="logistic2" and prior="normal", otherwise will be ignored.

beta_mean

The mean of parameter used when prior="exponential" or "normal", otherwise will be ignored.

a0

A constant value of intercept from a one-parameter logistic model only used when model="logistic" with default value 3, otherwise will be ignored.

alpha_shape

The shape parameter of intercept parameter only used when model="logistic2" and prior="gamma", otherwise will be ignored.

alpha_inverse_scale

The scale parameter of intercept parameter only used when model="logistic2" and prior="gamma", otherwise will be ignored.

beta_shape

The shape parameter used when prior="gamma", otherwise will be ignored.

beta_inverse_scale

The scale parameter used when prior="gamma", otherwise will be ignored.

References

Lee, S. M., & Cheung, Y. K. (2009). Model calibration in the continual reassessment method. Clinical Trials, 6(3), 227-238. tools:::Rd_expr_doi("10.1177/1740774509105076")

Examples

# generate skeleton based on empiric model with normal prior
prior <- priorSkeletons(updelta = 0.01, target = 0.25, npos= 5, ndose = 9, beta_mean = 0)

# generate skeleton based on one-parameter logistic model with normal prior
prior <- priorSkeletons(updelta = 0.01, target = 0.25, npos= 5, ndose = 9,
                        model = "logistic", prior = "normal", beta_mean = 0, a0 = 3)