Calculate a posterior distribution that is beta (or a mixture of beta components). Only the relevant treatment arms from the internal dataset should be read in (e.g., only the control arm if constructing a posterior distribution for the control response rate).
calc_post_beta(internal_data, response, prior)This can either be a propensity score object or a tibble of the internal data.
Name of response variable
A distributional object corresponding to a beta distribution or a mixture distribution of beta components
distributional object
For a given arm of an internal trial (e.g., the control arm or an active treatment arm) of size \(N_I\), suppose the response data are binary such that \(y_i \sim \mbox{Bernoulli}(\theta)\), \(i=1,\ldots,N_I\). The posterior distribution for \(\theta\) is written as
$$\pi( \theta \mid \boldsymbol{y} ) \propto \mathcal{L}(\theta \mid \boldsymbol{y}) \; \pi(\theta),$$
where \(\mathcal{L}(\theta \mid \boldsymbol{y})\) is the likelihood of the response data from the internal arm and \(\pi(\theta)\) is a prior distribution on \(\theta\) (either a beta distribution or a mixture distribution with an arbitrary number of beta components). The posterior distribution for \(\theta\) is either a beta distribution or a mixture of beta components depending on whether the prior is a single beta distribution or a mixture distribution.
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
library(distributional)
calc_post_beta(internal_data = filter(int_binary_df, trt == 1),
response = y,
prior = dist_beta(0.5, 0.5))
#> <distribution[1]>
#> [1] Beta(56, 26)