create_posterior_data: Creates posterior distributions for a range of weights on the...

In tipmap: Tipping Point Analysis for Bayesian Dynamic Borrowing

Creates posterior distributions for a range of weights on the informative component of the robust MAP prior

Description

Returns a data frame containing the default quantiles of posterior mixture distributions generated with varying weights on the informative component of the MAP prior.

Usage

create_posterior_data(map_prior, new_trial_data, sigma, null_effect = 0)

Arguments

map_prior	A MAP prior containing information about the trial(s) in the source population, created using RBesT.
new_trial_data	A vector containing information about the new trial. See create_new_trial_data().
sigma	Standard deviation to be used for the weakly informative component of the MAP prior, recommended to be the unit-information standard deviation.
null_effect	The mean of the robust component of the MAP prior. Defaults to 0.

Value

A data frame containing posterior distributions for varying weights

References

Best, N., Price, R. G., Pouliquen, I. J., & Keene, O. N. (2021). Assessing efficacy in important subgroups in confirmatory trials: An example using Bayesian dynamic borrowing. Pharm Stat, 20(3), 551–562. https://doi.org/10.1002/pst.2093

See Also

create_new_trial_data, create_prior_data

Examples

# create vector containing data on new trial
new_trial_data <- create_new_trial_data(
  n_total = 30,
  est = 1.27,
  se = 0.95
)

# read MAP prior created by RBesT
map_prior <- load_tipmap_data("tipmapPrior.rds")

# create posterior data
## Not run: 
posterior_data <- create_posterior_data(
  map_prior = map_prior,
  new_trial_data = new_trial_data,
  sigma = 12
)

## End(Not run)

tipmap documentation built on Aug. 14, 2023, 5:09 p.m.