alpha_discount {bayesDP}  R Documentation 
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 twoarm
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 
discount_function 
character. Specify the discount function to use.
Currently supports 
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
twoarm 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, 115.
Examples
alpha_discount(0.5)
alpha_discount(0.5, discount_function = "identity")