Safe anytime-valid inference: from theory to implementation in psychiatry research
Classical statistical methods, such as p-values, are difficult for researchers to apply correctly. They for example do not allow drawing conclusions from a study early, or for extending a study with extra research groups that want to make their data available later.
- R.J. Turner
- 14 november 2023
- Thesis in Leiden Repository
Sadly, in practice this often leads to faulty application of statistics and subsequent invalidity of experiment conclusions.Partly because of the above, recently, interest in safe, anytime-valid inference (SAVI) with e-values has emerged. This framework offers the same functionality as classical statistics, but also provides researchers with plenty of flexibility, for example through enabling early stopping and effect estimation at any time, extending a study in hindsight, and analyzing data located across multiple hospitals. In this thesis, this theory is further developed for performing SAVI in scenarios applicable to healthcare, specifically for several use-cases in psychiatry. It is explored how one could set up real-time psychiatry research in practice in an automated manner, combining text mining with network analysis techniques for data preparation and exploration and then confirming hypotheses with SAVI. Through this, the work in this thesis contributes to an environment where continuous learning from routinely collected healthcare data for better personalized recommendations is the new standard.