The tipmap
-package facilitates the planning and analysis of partial
extrapolation studies in pediatric drug development. It provides an
implementation of a Bayesian tipping point approach that can be 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 and the computation
of operating characteristics.
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")
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 informative component 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)
tipmap
To cite tipmap
in publications please use: Morten Dreher and Christian
Stock (2022). tipmap: Tipping Point Analysis for Bayesian Dynamic
Borrowing. R package version 0.4.2. URL:
https://CRAN.R-project.org/package=tipmap
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