create_posterior_data: Quantiles of posterior distributions for a range of weights...

In tipmap: Tipping Point Analysis for Bayesian Dynamic Borrowing

Quantiles of 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 or bounds of highest posterior density intervals, 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,
  interval_type = "equal-tailed",
  n_samples = 10000
)

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.
interval_type	The type of credible interval (character of length 1), either 'equal-tailed' (default) or 'hpdi', the highest posterior density interval.
n_samples	Number of samples to compute highest posterior density intervals (hence, only applicable if the 'interval_type' is 'hpdi').

Details

Highest posterior density intervals are based on 'coda::HPDinterval()' and are an experimental feature.

Value

A data frame containing the default quantiles of posterior mixture distributions or bounds of highest posterior density intervals.

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()] and [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 - with interval_type = equal_tailed
# (the default for tipping point plots)
posterior_data1 <- create_posterior_data(
  map_prior = map_prior,
  new_trial_data = new_trial_data,
  sigma = 12,
  interval_type = "equal-tailed"   
)


# create posterior data - with interval_type = hpdi
posterior_data2 <- create_posterior_data(
  map_prior = map_prior,
  new_trial_data = new_trial_data,
  sigma = 12,
  interval_type = "hpdi",
  n_samples = 1e4
)

tipmap documentation built on June 5, 2026, 9:12 a.m.