Robustify Normal Distributions

Adds vague normal component, where the level of vagueness is controlled by the n parameter

robustify_norm(prior, n, weights = c(0.5, 0.5))

Arguments

prior

Normal or Multivariate Normal distributional object

n

Number of theoretical participants (or events, for time-to-event data)

weights

Vector of weights, where the first number corresponds to the informative component and the second is the vague

Value

mixture distribution

Details

In cases with a normal endpoint, a robust mixture prior can be created by adding a vague normal component to any normal prior with mean \(\theta\) and variance \(\sigma^2\).The vague component is calculated to have the same mean \(\theta\) and variance equal to \(\sigma^2 \times n\), where n is the specified number of theoretical participants. If robustifying a normal power prior that was calculated from external control data and n is defined as the number of external control participants, and the vague component would then correspond to one external control participant's worth of data.

Examples

library(distributional)
robustify_norm(dist_normal(0,1), n = 15)
#> <distribution[1]>
#> [1] mixture(0.5*N(0, 1), 0.5*N(0, 15))