37 citations · 40 across the 7 of their papers we have counts for
8 papers
Bias through time-varying covariates in the analysis of cohort stepped wedge trials: a simulation study
Jale Basten, Katja Ickstadt, Nina Timmesfeld
In stepped wedge cluster randomized trials (SW-CRTs), observations collected under the control condition are, on average, from an earlier time than observations collected under the…
Cross-Leverage Scores for Selecting Subsets of Explanatory Variables
Katharina Parry, Leo N. Geppert, Alexander Munteanu +1
In a standard regression problem, we have a set of explanatory variables whose effect on some response vector is modeled. For wide binary data, such as genetic marker data, we ofte…
Bivariate Analysis of Birth Weight and Gestational Age Depending on Environmental Exposures: Bayesian Distributional Regression with Copulas
Jonathan Rathjens, Arthur Kolbe, Jürgen Hölzer +2
In this article, we analyze perinatal data with birth weight (BW) as primarily interesting response variable. Gestational age (GA) is usually an important covariate and included in…
Is there a role for statistics in artificial intelligence?
Sarah Friedrich, Gerd Antes, Sigrid Behr +11
The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion. Here we argue that statistics,…
Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression
Katrin Madjar, Manuela Zucknick, Katja Ickstadt +1
Important objectives in cancer research are the prediction of a patient's risk based on molecular measurements such as gene expression data and the identification of new prognostic…
Identifying treatment effect heterogeneity in dose-finding trials using Bayesian hierarchical models
Marius Thomas, Björn Bornkamp, Katja Ickstadt
An important task in drug development is to identify patients, which respond better or worse to an experimental treatment. Identifying predictive covariates, which influence the tr…
Beyond unimodal regression: modelling multimodality with piecewise unimodal regression or deconvolution models
Claudia Köllmann, Katja Ickstadt, Roland Fried
Shape constraints enable us to reflect prior knowledge in regression settings. A unimodality constraint, for example, can describe the frequent case of a first increasing and then…
Random projections for Bayesian regression
Leo N. Geppert, Katja Ickstadt, Alexander Munteanu +2
This article deals with random projections applied as a data reduction technique for Bayesian regression analysis. We show sufficient conditions under which the entire -dimensio…