SafetySignalDetection.jl

Blinded Analysis Model

This Turing model is used to generate posterior samples of the adverse event probabilities pi_exp in the experimental arm and pi_ctrl in the control arm given a blinded analysis of a trial with exp_proportion ratio of experimental arm patients relative to all patients.

blindedanalysismodel( y::Vector{Bool}, time::Vector{Float64}, priorexp::Distribution, priorctrl::Distribution, exp_proportion::Float64)

Blinded Analysis Posterior Samples Generation

This function wraps the Turing model blinded_analysis_model and runs it for a data frame df with: - y: Bool (did the adverse event occur?) - time: Float64 (time until adverse event or until last treatment or follow up)

Note that arguments for the number of samples per chain and the number of chains have to be passed as well.

It returns a DataFrame with the posterior samples for pi_exp and pi_ctrl.

Fit a beta mixture to a vector of prior samples

This function returns a beta mixture of n_components components approximating the distribution of the sample vector x.

Meta Analysis Model

This Turing model is used to generate posterior samples of the parameters a and b.

metaanalysismodel( y::Vector{Bool}, time::Vector{Float64}, trialindex::Vector{Int64}, priora::Distribution, priorb::Distribution)

Meta Analytic Prior Samples Generation

This function wraps the Turing model meta_analysis_model and runs it for a data frame df with: - y: Bool (did the adverse event occur?) - time: Float64 (time until adverse event or until last treatment or follow up) - trialindex: Int64 (index of trials, starting from 1 and consecutively numbered)

meta_analytic_samples(
df::DataFrame,
prior_a::Distribution, 
prior_b::Distribution, 
args...
)

Note that arguments for the number of samples per chain and the number of chains have to be passed as well.

It returns an array with the samples from the meta analytic prior (MAP).