10  What have you (not) learned?

We summarize some of what you have learned in this book and what else you may want to know.

10.1 Design via the web interface

You have now learned the basics of designing group sequential trials using the gsDesign web interface. This is specifically related to trials comparing two treatment groups with time-to-event outcomes, binomial outcomes, or normal outcomes. However, you have also learned about how to transform the sample size from a fixed design to a group sequential design. In addition, you have learned about information-based designs. We provided background on the use of spending functions and showed a variety of spending functions you might apply. We demonstrated tabular and graphical output available for summarizing the designs you derived. All that was required for any of the above was a web browser that could be on your computer, tablet, or phone.

10.2 Design via R programming

In Chapter 9, we extended what you could do with the web interface by teaching you some basics on applying the gsDesign package in R. We provided examples of how to analyze a group sequential trial and computing repeated confidence intervals. We discussed the concepts and showed how to compute conditional power, predictive power, probability of success, conditional probability of success, and prediction intervals. These are a lot of the basics you may want for applying group sequential designs. However, we also demonstrated two methods of sample size adaptation that can be derived using gsDesign: adaptation based on conditional power and adaptation based on observed statistical information.

10.3 Additional topics of interest

There are many topics not covered here that are often of interest to those applying group sequential designs. For instance, when a group sequential design crosses a bound at an interim analysis and the trial is stopped, how do you incorporate any data that was collected between the time of the database cutoff and the final analysis? This is a topic addressed by Whitehead (1992), among others.

You may be interested in simulation to see how the asymptotic theory used for designs here compares to exact inference; some basic capabilities for simulating trials comparing binomial rates are provided in the function simBinomial(). The simtrial package is also available for simulating time-to-event data.

There is also capability in gsDesign to design trials with an exact binomial outcome for evaluating, say, response rate in a single-arm oncology trial looking at response rates or a vaccine trial comparing rates of rare events between two treatment groups (Chan and Bohidar 1998). See ?gsBinomialExact after loading the gsDesign package in R for more on this topic. This function is also extended to sequential analysis in the function binomialSPRT(). This has been extended to designs with spending function bounds as demonstrated in https://keaven.github.io/gsDesign/articles/VaccineEfficacy.html.

10.4 Additional software

We have continued to update both the gsDesign R package and its web interface on a regular basis. The gsDesign2 package enables designs for time-to-event endpoints where there may be non-constant treatment effects during the course of the trial. This may be particularly helpful where a delayed treatment effect may be hypothesized; a web interface for this package is under development.

Other software we have co-developed includes the following R packages:

Finally, there are alternative software options in R for group sequential and adaptive design, such as rpact and RCTdesign, available under various licensing agreements.