Separate analysis

Introduction

This vignette describes an implementation of a Bayesian analysis without borrowing.

Set working directory and load packages

Load the case study configuration

Load the simulation configuration and the Belimumab case study configuration from YAML files.

set.seed(42)
case_study_config <- yaml::yaml.load_file(system.file("conf/case_studies/belimumab.yml", package = "BExTE"))

Create data objects

Create a source_data instance, where information about the source data is stored

source_data <- ObservedSourceData$new(case_study_config)

Set the observed target data (in the paediatrics population)

target_data <- ObservedTargetData$new(treatment_effect_estimate = case_study_config$target$treatment_effect, treatment_effect_standard_error = case_study_config$target$standard_error, target_sample_size_per_arm = as.integer(case_study_config$target$total / 2), summary_measure_likelihood = case_study_config$summary_measure_likelihood)

Create model

method <- "separate"
method_parameters <- list(
  initial_prior = "noninformative" # This corresponds to the fact that the posterior for the adults data is derived from an uninformative prior (for consistency with other methods).
)

Now, we define the model we want to use for inferring the treatment effect in the target study.

model <- Model$new()

model <- model$create(
  case_study_config = case_study_config,
  method = method,
  method_parameters = method_parameters,
  source_data = source_data
)

Inference

# Perform Bayesian inference based on observed target data.
model$inference(target_data = target_data)

## [1] "Success"