README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>", prompt = FALSE,
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# tipmap <img src="man/figures/logo.png" align="right" height="139" alt="" />

<!-- badges: start -->
[![](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)
[![](https://www.r-pkg.org/badges/version/tipmap)](https://cran.r-project.org/package=tipmap)
[![](http://cranlogs.r-pkg.org/badges/last-month/tipmap)](https://cran.r-project.org/package=tipmap)
[![](http://cranlogs.r-pkg.org/badges/grand-total/tipmap)](https://cran.r-project.org/package=tipmap)
<!-- badges: end -->

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.

## Installation

**CRAN**

You can install the current stable version from CRAN with:

```{r cran-installation, eval = FALSE}
install.packages("tipmap")
```

**GitHub**

You can install the current development version from GitHub with:

```{r gh-installation, eval = FALSE}
if (!require("remotes")) {install.packages("remotes")}
remotes::install_github("Boehringer-Ingelheim/tipmap")
```

## Getting started

Load the package:
```{r example01, eval = FALSE}
library(tipmap)
```

The prior data (collected in the source population):
```{r example02, eval = FALSE}
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):
```{r example03, eval = FALSE}
ped_trial <- create_new_trial_data(
  n_total = 30, 
  est = 1.27, 
  se = 0.95
)
```

Derivation of the meta-analytic predictive (MAP) prior:
```{r example04, eval = FALSE}
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)
  )
```

```{r example05, eval = FALSE}
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:
```{r example06, eval = FALSE}
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):
```{r example07, eval = FALSE}
tipmap_data <- create_tipmap_data(
  new_trial_data = ped_trial,
  posterior = posterior,
  map_prior = map_prior)
```

Create tipping point plot:
```{r example08, eval = FALSE}
tipmap_plot(tipmap_data = tipmap_data)
```

Get tipping points:
```{r example09, eval = FALSE}
get_tipping_points(
  tipmap_data, 
  quantile = c(0.025, 0.05, 0.1, 0.2), 
  null_effect = 0.1)
```



## Citing `tipmap` 

To cite `tipmap` in publications please use: Christian Stock and Morten Dreher (2023). tipmap: Tipping Point Analysis for Bayesian Dynamic Borrowing. R package version 0.5.2. URL: https://CRAN.R-project.org/package=tipmap