alpha_discount {bayesDP} | R Documentation |
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
)
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. |
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.
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.
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.
alpha_discount(0.5)
alpha_discount(0.5, discount_function = "identity")