The application of bespoke spending functions in group‐sequential designs and the effect of delayed treatment switching in survival trials

For a group-sequential trial with two pre-planned analyses, stopping boundaries can be calculated using a simple SAS programme on the basis of the asymptotic bivariate normality of the interim and final test statistics. Given the simplicity and transparency of this approach, it is appropriate for researchers to apply their own bespoke spending function as long as the rate of alpha spend is pre-specified. One such application could be an oncology trial where progression free survival (PFS) is the primary endpoint and overall survival (OS) is also assessed, both at the same time as the analysis of PFS and also later following further patient follow-up. In many circumstances it is likely, if PFS is significantly extended, that the protocol will be amended to allow patients in the control arm to start receiving the experimental regimen. Such an eventuality is likely to result in the diminution of any effect on OS. It is shown that spending a greater proportion of alpha at the first analysis of OS, using either Pocock or bespoke boundaries, will maintain and in some cases result in greater power given a fixed number of events.