1  Introduction

We begin with background, including an overview of group sequential designs for clinical trials, the gsDesign web interface, and the organization of this book.

1.1 Background

Group sequential design was first developed in the 1960s by Armitage, McPherson, and Rowe (1969), but became much more popular in the 1980s and 1990s as more methods became available (Pocock 1977; O’Brien and Fleming 1979). The group sequential designs presented here are all based on spending functions as developed by Lan and DeMets (1983). This provides a great deal of flexibility for the designs you may wish to derive. An excellent book on examples of interim monitoring applied to group sequential trials has been compiled by DeMets, Furberg, and Friedman (2006).

1.2 gsDesign web interface

Here we describe the gsDesign web interface that will allow you to generate group sequential designs simply without software other than a web browser. The software underlying this is the R package gsDesign. The web interface has been developed using the R web framework Shiny. The gsDesign web interface allows great flexibility in designing group sequential trials, including:

1. Time-to-event outcome trials, binomial outcome trials, and normal outcome trials.
2. Creating a group sequential design based on a fixed design sample size.
3. Flexible spending functions to derive design boundaries.

The web interface provides text and tabular output as well as a variety of graphs summarizing a design.

1.3 Recent updates to the gsDesign web interface

We have added an extensive set of new features and quality of life improvements to the web interface since 2021. Examples include:

1. Saving and re-loading designs.
2. Generation of R Markdown reports to document design; these can be saved in order to reproduce designs using the R package gsDesign.
3. Updating bounds at the time of analysis.
4. Setting timing for trials with a time-to-event endpoint using calendar timing.

We will describe these features in this book.

1.4 Additional topics covered

In addition to the web interface, Chapter 9 also demonstrates code for group sequential designs in R. The chapter also demonstrates several concepts and capabilities in the R package that have not been incorporated into the web interface:

1. Computing probability of success, conditional and predictive power and conditional probability of success.
2. Computing repeated confidence intervals and \(p\)-values.
3. How to update bounds if actual sample sizes or event counts at analyses differ from planned.
4. Adapting sample size using conditional power or information-based design.

Note that we have tried to keep any mathematical detail to a minimum. Here we focus on the concepts of group sequential design, providing references for those interested in more technical details. A classic reference for this is the book by Jennison and Turnbull (2000). A brief set of references is provided at the end of the book for those wishing to read further.

Armitage, Peter, CK McPherson, and BC Rowe. 1969. “Repeated Significance Tests on Accumulating Data.” Journal of the Royal Statistical Society: Series A (General) 132 (2): 235–44.

DeMets, David L, Curt D Furberg, and Lawrence M Friedman, eds. 2006. Data Monitoring in Clinical Trials: A Case Studies Approach. New York: Springer.

Jennison, Christopher, and Bruce W Turnbull. 2000. Group Sequential Methods with Applications to Clinical Trials. Boca Raton, FL: Chapman & Hall/CRC.

Lan, K K Gordon, and David L DeMets. 1983. “Discrete Sequential Boundaries for Clinical Trials.” Biometrika 70 (3): 659–63.

O’Brien, Peter C, and Thomas R Fleming. 1979. “A Multiple Testing Procedure for Clinical Trials.” Biometrics 35 (3): 549–56.

Pocock, Stuart J. 1977. “Group Sequential Methods in the Design and Analysis of Clinical Trials.” Biometrika 64 (2): 191–99.