27 citations · 30 across the 6 of their papers we have counts for
6 papers
A Cautionary Tale About "Neutrally" Informative AI Tools Ahead of the 2025 Federal Elections in Germany
Ina Dormuth, Sven Franke, Marlies Hafer +6
In this study, we examine the reliability of AI-based Voting Advice Applications (VAAs) and large language models (LLMs) in providing objective political information. Our analysis…
Single CASANOVA? Not in multiple comparisons
Ina Dormuth, Carolin Herrmann, Frank Konietschke +3
When comparing multiple groups in clinical trials, we are not only interested in whether there is a difference between any groups but rather the location. Such research questions l…
Adaptive weight selection for time-to-event data under non-proportional hazards
Moritz Fabian Danzer, Ina Dormuth
When planning a clinical trial for a time-to-event endpoint, we require an estimated effect size and need to consider the type of effect. Usually, an effect of proportional hazards…
How to Simulate Realistic Survival Data? A Simulation Study to Compare Realistic Simulation Models
Maria Thurow, Ina Dormuth, Christina Sauer +2
In statistics, it is important to have realistic data sets available for a particular context to allow an appropriate and objective method comparison. For many use cases, benchmark…
The Self-Perception and Political Biases of ChatGPT
Jérôme Rutinowski, Sven Franke, Jan Endendyk +2
This contribution analyzes the self-perception and political biases of OpenAI's Large Language Model ChatGPT. Taking into account the first small-scale reports and studies that hav…
A comparative study to alternatives to the log-rank test
Ina Dormuth, Tiantian Liu, Jin Xu +2
Studies to compare the survival of two or more groups using time-to-event data are of high importance in medical research. The gold standard is the log-rank test, which is optimal…