News
New team members
In April 2024, Dirk Schomburg and Simon Mack joined our team. Welcome!
New Consistency proofs for tree-based methods
Nico Föge finished his first phD project regarding machine learning. Concratulations!!
- Föge, Pauly, Schmid and Ditzhaus (2024). From naive trees to Random Forests: A general approach for proving consistency of tree-based methods (arXiv: 2404.06850)
RMST methods
New progress in our DFG project on Survival Analysis. Merle´s paper about the RMST in factorial designs and a corresponing R package are published:
- GFDrmst: available on CRAN. This package consists multiple tests based on the restricted mean survival time (RMST) for general factorial designs as described in Munko et al. (2024, Stat. Med.)
- , , , . RMST-based multiple contrast tests in general factorial designs. Statistics in Medicine. 2024; 1-18. doi: 10.1002/sim.10017 (Open Access)
Three new publications
A successful start in 2023 with three accepted papers:
- Matabuena, Félix, Ditzhaus, Vidal and Gude. Hypothesis testing for matched pairs with missing data by maximum mean discrepancy: An application to continuous glucose monitoring. Accept by The American Statistician (preliminary arXiv-Version)
- RKHS-procedure for complex missing data scenarios
- Dormuth, Liu, Xu, Pauly, Ditzhaus. A comparitive study to alternatives to the log-rank test. Accepted by Contemporary Clinical Trials (prelininary arXiv-preprint)
- Comparison of novel two-sample tests for time-to-event data when the hazards are not proportional
- Ditzhaus, Yu and Xu. Studentized Permutation Method for Comparing Restricted Mean Survival Times with Small Sample from Randomized Trials. Accepted by Statistics in Medicine (preliminary arXiv-preprint).
- Two-sample test procedure and confidence intervalls for the restriced mean survival time when the sample size is small.