alpha_discount: Bayesian Discount Prior: Historical Data Weight (alpha)

In bayesDP: Implementation of the Bayesian Discount Prior Approach for Clinical Trials

Bayesian Discount Prior: Historical Data Weight (alpha)

Description

alpha_discount can be used to estimate the weight applied to historical data in the context of a one- or two-arm clinical trial. alpha_discount is not used internally but is given for educational purposes.

Usage

alpha_discount(
  p_hat = NULL,
  discount_function = "weibull",
  alpha_max = 1,
  weibull_scale = 0.135,
  weibull_shape = 3
)

Arguments

p_hat	scalar. The posterior probability of a stochastic comparison. This value can be the output of posterior_probability or a value between 0 and 1.
discount_function	character. Specify the discount function to use. Currently supports weibull, scaledweibull, and identity. The discount function scaledweibull scales the output of the Weibull CDF to have a max value of 1. The identity discount function uses the posterior probability directly as the discount weight. Default value is "weibull".
alpha_max	scalar. Maximum weight the discount function can apply. Default is 1.
weibull_scale	scalar. Scale parameter of the Weibull discount function used to compute alpha, the weight parameter of the historical data. Default value is 0.135.
weibull_shape	scalar. Shape parameter of the Weibull discount function used to compute alpha, the weight parameter of the historical data. Default value is 3.

Details

This function is not used internally but is given for educational purposes. Given inputs p_hat, discount_function, alpha_max, weibull_shape, and weibull_scale the output is the weight that would be applied to historical data in the context of a one- or two-arm clinical trial.

Value

alpha_discount returns an object of class "alpha_discount".

An object of class alpha_discount contains the following:

alpha_hat

scalar. The historical data weight.

References

Haddad, T., Himes, A., Thompson, L., Irony, T., Nair, R. MDIC Computer Modeling and Simulation working group.(2017) Incorporation of stochastic engineering models as prior information in Bayesian medical device trials. Journal of Biopharmaceutical Statistics, 1-15.

Examples

alpha_discount(0.5)

alpha_discount(0.5, discount_function = "identity")

bayesDP documentation built on March 18, 2022, 7:41 p.m.