|#||Title||Category Sort descending||Endpoint||Summary|
|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
|1||Defining Group Sequential Boundaries with rpact||Planning||Categorical, Continuous, Survival|
|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
|6||An Example to Illustrate Boundary Re-Calculations during the Trial with rpact||Planning||Survival||
This R Markdown document provides an example for updating the group sequential boundaries when using an alpha-spending function approach based on observed information rates in rpact. Since version 3.1 of rpact, an additional option in the
|5||Simulation-Based Design of Group Sequential Trials with a Survival Endpoint with rpact||Planning||Survival||Power simulation|
|11||Comparing Sample Size and Power Calculation Results for a Group Sequential Trial with a Survival Endpoint: rpact vs. gsDesign||Planning||Survival|
|2||Designing Group Sequential Trials with Two Groups and a Continuous Endpoint with rpact||Planning||Continuous|
|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.
|12||Supplementing and Enhancing rpact’s Graphical Capabilities with ggplot2||Planning||Continuous||Power simulation|
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