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tipmap

Thetipmap-package facilitates the planning and analysisof partial extrapolation studies in pediatric drug development. Itprovides an implementation of a Bayesian tipping point approach that canbe used in analyses based on robust meta-analytic predictive (MAP)priors. Further functions facilitate expert elicitation of a primary(pre-specified) weight of the informative component of the MAP prior andthe computation of operating characteristics.

Installation

CRAN

You can install the current stable version from CRAN with:

install.packages("tipmap")

GitHub

You can install the current development version from GitHub with:

if (!require("remotes")) {install.packages("remotes")}remotes::install_github("Boehringer-Ingelheim/tipmap")

Getting started

Load the package:

library(tipmap)

The prior data (collected in the source population):

prior_data<-create_prior_data(n_total =c(160,240,320),est =c(1.23,1.40,1.51),se =c(0.4,0.36,0.31))

The data from the new trial (collected in the target population):

ped_trial<-create_new_trial_data(n_total =30,est =1.27,se =0.95)

Derivation of the meta-analytic predictive (MAP) prior:

uisd<-sqrt(ped_trial["n_total"])* ped_trial["se"]g_map<-  RBesT::gMAP(formula =cbind(est, se)~1| study_label,data = prior_data,family = gaussian,weights = n_total,tau.dist ="HalfNormal",tau.prior =cbind(0, uisd/16),beta.prior =cbind(0, uisd)  )
map_prior<- RBesT::automixfit(sample = g_map,Nc =seq(1,4),k =6,thresh =-Inf)

Computing the posterior distribution for weights of the informativecomponent of the MAP prior ranging from 0 to 1:

posterior<-create_posterior_data(map_prior = map_prior,new_trial_data = ped_trial,sigma = uisd)

Creating data for a tipping point analysis (tipping point plot):

tipmap_data<-create_tipmap_data(new_trial_data = ped_trial,posterior = posterior,map_prior = map_prior)

Create tipping point plot:

tipmap_plot(tipmap_data = tipmap_data)

Get tipping points:

get_tipping_points(  tipmap_data,quantile =c(0.025,0.05,0.1,0.2),null_effect =0.1)

Citingtipmap

To citetipmap in publications please use: Morten Dreherand Christian Stock (2022). tipmap: Tipping Point Analysis for BayesianDynamic Borrowing. R package version 0.4.2. URL:https://CRAN.R-project.org/package=tipmap


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