These are probably simple concepts for you guys but I am trying to understand the practicalities of analysing data from a group-sequential design where the outcome is continuous and you plan to measure each participant at multiple time points (longitudinal). Say you plan a trial with 3 interim analyses and stopping rules for efficacy. At the 2nd look (50% of the complete data available), you have evidence of efficacy and terminate the study.
In a fixed sample design one might analyse the treatment effect for the primary endpoint using a mixed model with a treatment x time interaction term. I read somewhere that naive treatment effects are biased in group sequential designs if terminating early (or even at completion)? Does this mean that the typical approach of using said mixed model with treatment by time interaction won’t work and some other method needs to be used to estimate an unbiased treatment effect? (If so, how is this done in R)
Hope that makes sense and I am not confusing some basic concepts.