Abstract
Lan and DeMets (Biometrika 1983; 70: 659-663) introduced a flexible procedure for monitoring of group sequential clinical trials based on the discretization of the Brownian motion process. Subsequently Kim and DeMets (Biometrika 1987; 74: 149-154) developed a general procedure for design of such clinical trials. A number of procedures have been proposed for statistical inference following group sequential tests regarding the P-values and the point and confidence interval estimation of the parameter of interest such as the effect size or the treatment difference in such clinical trials. In this article, computational issues are described for design and monitoring of clinical trials with interim analysis based on group sequential methods for possible early stopping for efficacy or safety and for inference following early stopping of group sequential clinical trials. The computational procedures as implemented in a commercial package EaSt (2000) are illustrated with an example of a lung cancer clinical trial
