probability_discount function

Bayesian Discount Prior: Comparison Between Current and Historical Data

probability_discount can be used to estimate the posterior probability of the comparison between historical and current data in the context of a clinical trial with normal (mean) data. probability_discount is not used internally but is given for educational purposes.

probability_discount(
  mu = NULL,
  sigma = NULL,
  N = NULL,
  mu0 = NULL,
  sigma0 = NULL,
  N0 = NULL,
  number_mcmc = 10000,
  method = "fixed"
)

Arguments

• mu: scalar. Mean of the current data.

• sigma: scalar. Standard deviation of the current data.

• N: scalar. Number of observations of the current data.

• mu0: scalar. Mean of the historical data.

• sigma0: scalar. Standard deviation of the historical data.

• N0: scalar. Number of observations of the historical data.

• number_mcmc: scalar. Number of Monte Carlo simulations. Default is 10000.

• method: character. Analysis method. Default value "fixed" estimates the posterior probability and holds it fixed. Alternative method "mc" estimates the posterior probability for each Monte Carlo iteration. See the the bdpnormal vignette

  vignette("bdpnormal-vignette", package="bayesDP") for more details.

Returns

probability_discount returns an object of class "probability_discount".

An object of class probability_discount contains the following:

• p_hat: scalar. The posterior probability of the comparison historical data weight. If method="mc", a vector of posterior probabilities of length number_mcmc is returned.

Details

This function is not used internally but is given for educational purposes. Given the inputs, the output is the posterior probability of the comparison between current and historical data in the context of a clinical trial with normal (mean) data.

Examples

probability_discount(
  mu = 0, sigma = 1, N = 100,
  mu0 = 0.1, sigma0 = 1, N0 = 100
)

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.