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.
# Sort ascending | Title | Category | Endpoint | Summary |
---|---|---|---|---|
26 | Delayed Response Designs with rpact | Planning | Categorical, Continuous, Survival | This R Markdown document provides a brief introduction to group sequential designs with delayed responses as proposed by Hampson and Jennison (2013). It is shown how this is implemented in rpact. Examples for designing trials with delayed responses using the software are provided. We also describe an alternative approach that directly uses the α-spending approach to derive the decision boundaries. |
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 |
22 | Step-by-Step rpact Tutorial | Getting started | Categorical, Continuous, Survival | The R package rpact has been developed to design sequential and adaptive experiments. Many of the functions of the R package are available in an online Shiny app. For more information about |
21 | Analysis of a Multi-Arm Design with a Binary Endpoint using rpact | Analysis | Categorical | This R Markdown document shows how to analyse and interpret multi-arm designs for testing proportions with rpact. 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 |
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 |
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 |
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 |
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 |
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 |
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