most citedA non-parametric proportional risk model to assess a treatment effect in time-to-event data

1 citations · 1 across the 5 of their papers we have counts for

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5 papers

stat.ME2024

Identification of changes in gene expression

Lucia Ameis, Kathrin Möllenhoff

Evaluating the change in gene expression is a common goal in many research areas, such as in toxicological studies as well as in clinical trials. In practice, the analysis is often…

stat.ME2024

Testing for similarity of multivariate mixed outcomes using generalised joint regression models with application to efficacy-toxicity responses

Niklas Hagemann, Giampiero Marra, Frank Bretz +1

A common problem in clinical trials is to test whether the effect of an explanatory variable on a response of interest is similar between two groups, e.g. patient or treatment grou…

stat.ME2024

Testing similarity of parametric competing risks models for identifying potentially similar pathways in healthcare

Kathrin Möllenhoff, Nadine Binder, Holger Dette

The identification of similar patient pathways is a crucial task in healthcare analytics. A flexible tool to address this issue are parametric competing risks models, where transit…

stat.ME20231 cited

A non-parametric proportional risk model to assess a treatment effect in time-to-event data

Lucia Ameis, Oliver Kuß, Annika Hoyer +1

Time-to-event analysis often relies on prior parametric assumptions, or, if a non-parametric approach is chosen, Cox's model. This is inherently tied to the assumption of proportio…

math.ST2023

Comparing regression curves -- an -point of view

Patrick Bastian, Holger Dette, Lukas Koletzko +1

In this paper we compare two regression curves by measuring their difference by the area between the two curves, represented by their -distance. We develop asymptotic confiden…