Clinical trials conducted during the COVID-19 pandemic demonstrated the value of adaptive design methods in emerging disease settings, when there can be considerable uncertainty around disease natural history, anticipated endpoint effect sizes and population size. In such settings, there may also be uncertainty regarding the most appropriate primary endpoint. This might lead to an externally-driven decision to change the primary endpoint during the course of an adaptive trial. If information on the new primary endpoint is already being collected, initially as a secondary endpoint, the trial could continue with a new primary endpoint. In this case it is unclear how statistical inference on the final primary endpoint should be adjusted for interim analyses monitoring the initial primary endpoint so as to control the overall type I error rate as adjusting for monitoring as if this was based on the new endpoint could be conservative whereas failing to make any adjustment could lead to type I error rate inflation if the new and original endpoint are correlated. This paper shows how group-sequential methods can be modified to control the type I error rate for the analysis of the new primary endpoint irrespective of the true treatment effect on the initial primary endpoint. The method is illustrated using a simulated data example based on a clinical trial of remdesivir in COVID-19. Construction of critical values for the test of the new primary endpoint require a value for the correlation between this and the initial primary endpoint. We present simulation studies to demonstrate that the type I error rate is controlled when this value is estimated from the data on the two endpoints obtained from the trial.