Safe Sequential Testing and Effect Estimation in Stratified Count Data
Abstract
Sequential decision making significantly speeds up research and is more cost-effective compared to fixed-n methods. We present a method for sequential decision making for stratified count data that retains Type-I error guarantee or false discovery rate under optional stopping, using e-variables. We invert the method to construct stratified anytime-valid confidence sequences, where cross-talk between subpopulations in the data can be allowed during data collection to improve power. Finally, we combine information collected in separate subpopulations through pseudo-Bayesian averaging and switching to create effective estimates for the minimal, mean and maximal treatment effects in the subpopulations.
Cite
@article{arxiv.2302.11401,
title = {Safe Sequential Testing and Effect Estimation in Stratified Count Data},
author = {Rosanne J. Turner and Peter D. Grünwald},
journal= {arXiv preprint arXiv:2302.11401},
year = {2023}
}
Comments
Preprint, to be published in the Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS) 2023, Valencia, Spain. PMLR: Volume 206