The `tipmap`

package aims to facilitate the planning and
analysis of partial extrapolation studies in pediatric drug development.
It provides an implementation of a Bayesian tipping point approach based
on robust meta-analytic predictive (MAP) priors, with further functions
facilitating expert elicitation of a primary weight of the informative
component of the prior.

To install `tipmap`

use:

`install.packages("tipmap")`

Suppose that data from three clinical trials in adults is to be combined with data from a small pediatric trial using a robust MAP prior approach.

```
library(tipmap)
<- createNewTrialData(
pediatricTrial nTotal = 30,
treatmentEffectEstimate = 1.27,
standardError = 0.95
)
<- sqrt(pediatricTrial["nTotal"]) * pediatricTrial["SE"]
uisd
<- createPriorData(
priorData nTotal = c(160, 240, 320),
treatmentEffectEstimate = c(1.23, 1.40, 1.51),
standardError = c(0.4, 0.36, 0.31)
)
<-
gMap ::gMAP(
RBesTformula = cbind(treatmentEffectEstimate, standardError) ~ 1 |
studyLabel,data = priorData,
family = gaussian,
weights = nTotal,
tau.dist = "HalfNormal",
tau.prior = cbind(0, uisd / 16),
beta.prior = cbind(0, uisd)
)
<-
mapPrior ::automixfit(
RBesTsample = gMap,
Nc = seq(1, 4),
k = 6,
thresh = -Inf
)
<- createPosteriorData(mapPrior = mapPrior,
posterior newTrialData = pediatricTrial,
sigma = uisd)
<-
tippingPointData createTippingPointData(newTrialData = pediatricTrial,
posterior = posterior,
mapPrior = mapPrior)
tippingPointPlot(tippingPointData = tippingPointData)
getTippingPoint(tippingPointData = tippingPointData,
quantile = c(0.05, 0.025))
```