Bringing Sequential Testing to Experiments with Longitudinal Data (Part 2): Sequential Testing

This post details the implementation of sequential testing for experiments using longitudinal data. It addresses the 'peeking problem 2.0' by explaining the increased complexity in the dependence structure of test statistics due to multiple observations per unit. The post advocates for a shift in mindset from 'metrics' to 'models', leveraging panel data modeling techniques. It discusses practical considerations like data compression for sufficient statistics and introduces estimators like ROB-OLS, ROB-WLS, and GLS for modeling treatment effects in longitudinal experiments, enabling the use of all available data at all intermittent analyses.