priorSkeletons {crm12Comb}

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)

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.

Value

A vector of length ndose is returned.

Note

The skeletons can be either specified by clinical researchers based on history information or directly generated based on this function given specific model and prior distribution.

References

Lee, S. M., & Cheung, Y. K. (2009). Model calibration in the continual reassessment method. Clinical Trials, 6(3), 227-238. 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)