Vignettes

Developed for practical use: below you find a collection of practical examples and use-cases, the so-called rpact vignettes.

In addition to these public open access vignettes, our RPACT SLA customers have access to exclusive vignettes on special topics such as the analysis of multi-stage data with covariates from raw data.

# Title Sort descending Category Endpoint Summary
10 How to Create Admirable Plots with rpact Utilities Categorical, Continuous, Survival

This R Markdown document provides many different examples for creating plots with rpact and ggplot2, e.g. the plot argument type will be illustrated.

Sample size, Power simulation, Power
18 How to Create One- and Multi-Arm Analysis Result Plots with rpact Utilities Categorical, Continuous, Survival

This R Markdown document provides many different examples for creating one- and multi-arm analysis result plots with rpact and ggplot2.

Analysis
19 How to Create One- and Multi-Arm Simulation Result Plots with rpact Utilities Categorical, Continuous, Survival

This R Markdown document provides many different examples for creating one- and multi-arm simulation result plots with rpact and ggplot2.

Power simulation, Multi-arm, Planning
17 How to Create Summaries with rpact Utilities Categorical, Continuous, Survival

This R Markdown document provides many different examples that illustrate the usage of the R generic function summary with rpact. This is a technical vignette and is to be considered mainly as a comprehensive overview of the possible summaries in rpact.

9 How to use R Generics with rpact Utilities Categorical, Continuous, Survival

This R Markdown document provides many different examples that illustrate the usage of so-called R generic functions (short: R generics) with rpact, e.g., as.data.frame or summary.

15 Planning a Survival Trial with rpact Planning Survival

This R Markdown document provides an example for planning a trial with a survival endpoint using rpact thereby illustrating the different ways of entering recruitment schemes. It also demonstrates the use of the survival simulation function.

Power simulation, Sample size
14 Planning a Trial with Binary Endpoints with rpact Planning Categorical

This R Markdown document provides an example for planning a trial with a binary endpoint using rpact. It also illustrates the use of ggplot2 for illustrating the characteristics of a sample size recalculation strategy. Another example for planning a trial with binary endpoints can be found in the vignette Designing group sequential trials with a binary endpoint with rpact.

Sample size, Power simulation
23 Planning and Analyzing a Group-Sequential Multi-Arm Multi-Stage Design with Binary Endpoint using rpact Getting started Categorical

This R Markdown document provides an example of implementing, simulating and analyzing multi-arm-multi-stage (MAMS) designs for testing rates with rpact with special regards to futility bound determination, treatment arm selection and generic data analysis.

After exemplarily using the binary endpoint analysis module from rpact, an illustrative landmark analysis (comparison of empirical survival probabilities at specific time point) using Greenwoods standard error estimation is to be performed. Since rpact itself does not directly support this type of analysis, another packages’ functionality needs to be utilized to perform the survival probability and standard error estimation to eventually use the estimates as input for a hypothetical continuous endpoint dataset which subsequently is to be analyzed as such.

Multi-arm
20 Simulating Multi-Arm Designs with a Continuous Endpoint using rpact Planning Continuous

This R Markdown document provides examples for simulating multi-arm multi-stage (MAMS) designs for testing means with rpact.

Power simulation, Multi-arm
16 Simulation of a Trial with a Binary Endpoint and Unblinded Sample Size Re-Calculation with rpact Planning Categorical

This R Markdown document provides examples for assessing trials with adaptive sample size re-calculation (SSR) using rpact. It also shows how to implement the promizing zone approach as proposed by Mehta and Pocock 2011 and further developed by Hsiao et al 2019 with rpact.

Power simulation

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