- As soon as positive results are found in the trial (for example, a drug shows statistically significant benefits), the trial is continued.
- As soon as negative results are found (for example, adverse effects), the trial is ended, so the trial could be stopped at any point in time.
Interim Analysis in Group Sequential Design
The circumstances under which the trial will be stopped or continued is specified in advance (Kelly et. al, 2005). A group sequential design includes a pre-determined number of stages, including interim stages (with the associated interim analysis) and a final stage. Each stage is specified by:
- The sample size,
- Critical Values,
- A stopping criterion to either support or reject the null hypothesis.
At the end of each interim stage, data analysis is usually performed or reviewed by a Data and Safety Monitoring Committee (DSMC). Interim analysis involves calculation of a test statistic. This is compared to the critical value to decide whether to stop or continue the trial.
Comparison to Fixed Sample Designs
The typical fixed sample design has patients entering the trial sequentially in matched pairs for two different treatments; Data is only analyzed once at the conclusion of the trial. Group sequential has patients entering in groups; Data is analyzed at a certain number of specified stopping points.
Kelly et. al (2005). A practical comparison of group-sequential and adaptive designs. J Biopharm Stat. 2005;15(4):719-38.
Pocock (1977). Group sequential methods in the design and analysis of clinical trials. Biometrika. 64, 2, pp. 191-9.
Chow & Lu. (2008). Design and Analysis of Clinical Trials: Concepts and Methodologies.
Need help with a homework or test question? With Chegg Study, you can get step-by-step solutions to your questions from an expert in the field. If you rather get 1:1 study help, Chegg Tutors offers 30 minutes of free tutoring to new users, so you can try them out before committing to a subscription.
If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.
Comments? Need to post a correction? Please post a comment on our Facebook page.