alpha_discount function

Bayesian Discount Prior: Historical Data Weight (alpha)

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.

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.

Returns

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.

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.

Examples

alpha_discount(0.5)

alpha_discount(0.5, discount_function = "identity")

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.