---
title: "Getting started with rpact"
author: "Friedrich Pahlke and Gernot Wassmer"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting started with rpact}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

**Confirmatory Adaptive Clinical Trial Design, Simulation, and Analysis**

## Functional Range

*	Fixed sample design and designs with interim analysis stages
*	Sample size and power calculation for
    +	means (continuous endpoint)
    +	rates (binary endpoint)
    +	survival trials with flexible recruitment and survival time options
    +	count data 
*	Simulation tool for means, rates, survival data, and count data
    +	Assessment of adaptive sample size/event number recalculations based on
        conditional power
    +   Assessment of treatment selection strategies in multi-arm trials
*	Adaptive analysis of means, rates, and survival data
*	Adaptive designs and analysis for multi-arm trials
*	Adaptive analysis and simulation tools for enrichment design testing means,
    rates, and hazard ratios
*	Automatic boundary recalculations during the trial for analysis with alpha
    spending approach, including under- and over-running
  

## Learn to use rpact

We recommend three ways to learn how to use `rpact`:

> 1. Use the Shiny app: [shiny.rpact.com](https://www.rpact.com/products/#public-rpact-shiny-app)
> 2. Use the Vignettes:
>    [www.rpact.org/vignettes](https://www.rpact.org/vignettes/)
> 3. Book a training:
>    [www.rpact.com](https://www.rpact.com/services/#learning-and-training)

### Vignettes

The vignettes are hosted at
[www.rpact.org/vignettes](https://www.rpact.org/vignettes/) and cover the
following topics:

1. Defining Group Sequential Boundaries with rpact
2. Designing Group Sequential Trials with Two Groups and a Continuous Endpoint
   with rpact
3. Designing Group Sequential Trials with a Binary Endpoint with rpact
4. Designing Group Sequential Trials with Two Groups and a Survival Endpoint
   with rpact
5. Simulation-Based Design of Group Sequential Trials with a Survival Endpoint
   with rpact
6. An Example to Illustrate Boundary Re-Calculations during the Trial with rpact
7. Analysis of a Group Sequential Trial with a Survival Endpoint using rpact
8. Defining Accrual Time and Accrual Intensity with rpact
9. How to use R Generics with rpact
10. How to Create Admirable Plots with rpact
11. Comparing Sample Size and Power Calculation Results for a Group Sequential
    Trial with a Survival Endpoint: rpact vs. gsDesign
12. Supplementing and Enhancing rpact’s Graphical Capabilities with ggplot2
13. Using the Inverse Normal Combination Test for Analyzing a Trial with
    Continuous Endpoint and Potential Sample Size Re-Assessment with rpact
14. Planning a Trial with Binary Endpoints with rpact
15. Planning a Survival Trial with rpact
16. Simulation of a Trial with a Binary Endpoint and Unblinded Sample Size
    Re-Calculation with rpact
17. How to Create Summaries with rpact
18. How to Create One- and Multi-Arm Analysis Result Plots with rpact
19. How to Create One- and Multi-Arm Simulation Result Plots with rpact
20. Simulating Multi-Arm Designs with a Continuous Endpoint using rpact
21. Analysis of a Multi-Arm Design with a Binary Endpoint using rpact
22. Step-by-Step rpact Tutorial
23. Planning and Analyzing a Group-Sequential Multi-Arm Multi-Stage Design with
    Binary Endpoint using rpact   
24. Two-arm Analysis for Continuous Data with Covariates from Raw Data using
    rpact (*exclusive*)
25. How to Install the Latest rpact Developer Version (*exclusive*)
26. Delayed Response Designs with rpact
27. Sample Size Calculation for Count Data

## User Concept

### Workflow

* Everything is starting with a design, e.g.: 
`design <- getDesignGroupSequential()`
* Find the optimal design parameters with help of `rpact` comparison tools:
`getDesignSet`
* Calculate the required sample size, e.g.: `getSampleSizeMeans()`,
`getPowerMeans()`
* Simulate specific characteristics of an adaptive design, e.g.:
`getSimulationMeans()` 
* Collect your data, import it into R and create a dataset:
  `data <- getDataset()` 
* Analyze your data: `getAnalysisResults(design, data)`

### Focus on Usability

The most important `rpact` functions have intuitive names:

* `getDesign`[`GroupSequential`/`InverseNormal`/`Fisher`]`()`
* `getDesignCharacteristics()`
* `getSampleSize`[`Means`/`Rates`/`Survival`/`Counts`]`()`
* `getPower`[`Means`/`Rates`/`Survival`/`Counts`]`()`
* `getSimulation`[`MultiArm`/`Enrichment`]``[`Means`/`Rates`/`Survival`]`()`
* `getDataSet()`
* `getAnalysisResults()`
* `getStageResults()`

RStudio/Eclipse: auto code completion makes it easy to use these functions.

### R generics

In general, everything runs with the R standard functions which are always
present in R: so-called R generics, e.g., `print`, `summary`, `plot`,
`as.data.frame`, `names`, `length`

### Utilities

Several utility functions are available, e.g.

* `getAccrualTime()`
* `getPiecewiseSurvivalTime()`
* `getNumberOfSubjects()`
* `getEventProbabilities()`
* `getPiecewiseExponentialDistribution()`
* survival helper functions for conversion of `pi`, `lambda` and `median`, e.g.,
  `getLambdaByMedian()`
* `testPackage()`: installation qualification on a client computer or company
  server (via unit tests)

## Validation

Please [contact](https://www.rpact.com/contact/) us to learn how to use `rpact`
on FDA/GxP-compliant validated corporate computer systems and how to get a copy
of the formal validation documentation that is customized and licensed for
exclusive use by your company, e.g., to fulfill regulatory requirements.

## RPACT Connect 

Connecting you to insights, downloads, and premium support:
[connect.rpact.com](https://rpact.shinyapps.io/connect)

## About

* **rpact** is a comprehensive validated^[The rpact validation documentation is
  available exclusively for our customers and supporting companies. For more
  information visit
  [www.rpact.com/services/sla](https://www.rpact.com/services/service-level-agreement/)] R package
  for clinical research which
    + enables the design and analysis of confirmatory adaptive group sequential
      designs
    + is a powerful sample size calculator
    + is a free of charge open-source software licensed under
      [LGPL-3](https://cran.r-project.org/web/licenses/LGPL-3)
    + particularly, implements the methods described in the recent monograph by
      [Wassmer and Brannath (2016)](https://doi.org/10.1007%2F978-3-319-32562-0)

> For more information please visit [www.rpact.org](https://www.rpact.org)

* **RPACT** is a company which offers
    + enterprise software development services 
    + technical support for the `rpact` package
    + consultancy and user training for clinical research using R
    + validated software solutions and R package development for clinical
      research

> For more information please visit [www.rpact.com](https://www.rpact.com)

## Contact

* [info@rpact.com](mailto:info@rpact.com)
* [www.rpact.com/contact](https://www.rpact.com/contact/)
