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Heidi Seibold
Heidi Seibold
Open Science Trainer
Verified email at seibold.co - Homepage
Title
Cited by
Cited by
Year
Model-Based Recursive Partitioning for Subgroup Analyses
H Seibold, A Zeileis, T Hothorn
The international journal of biostatistics 12 (1), 45-63, 2016
1692016
Individual treatment effect prediction for amyotrophic lateral sclerosis patients
H Seibold, A Zeileis, T Hothorn
Statistical methods in medical research 27 (10), 3104-3125, 2018
672018
OpenML: An R package to connect to the machine learning platform OpenML
G Casalicchio, J Bossek, M Lang, D Kirchhoff, P Kerschke, B Hofner, ...
Computational Statistics, 1-15, 2017
652017
Invertebrates outcompete vertebrate facultative scavengers in simulated lynx kills in the Bavarian Forest National Park, Germany
RR Ray, H Seibold, M Heurich
Animal Biodiversity and Conservation 37 (1), 77-88, 2014
632014
An environment for sustainable research software in Germany and beyond: current state, open challenges, and call for action
H Anzt, F Bach, S Druskat, F Löffler, A Loewe, BY Renard, G Seemann, ...
F1000Research 9, 2020
552020
Open science in software engineering
D Mendez, D Graziotin, S Wagner, H Seibold
Contemporary Empirical Methods in Software Engineering, 477-501, 2020
522020
Patterns of lynx predation at the interface between protected areas and multi-use landscapes in central Europe
E Belotti, N Weder, L Bufka, A Kaldhusdal, H Küchenhoff, H Seibold, ...
PloS one 10 (9), e0138139, 2015
402015
Subgroup identification in clinical trials: an overview of available methods and their implementations with R
Z Zhang, H Seibold, MV Vettore, WJ Song, V François
Annals of Translational Medicine 6 (7), 2018
322018
A replication crisis in methodological research?
AL Boulesteix, S Hoffmann, A Charlton, H Seibold
Significance 17 (5), 18-21, 2020
292020
Generalised linear model trees with global additive effects
H Seibold, T Hothorn, A Zeileis
Advances in Data Analysis and Classification 13 (3), 703-725, 2019
282019
On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models
H Seibold, C Bernau, AL Boulesteix, R De Bin
Computational Statistics, 1-21, 2018
28*2018
Subgroup identification in dose‐finding trials via model‐based recursive partitioning
M Thomas, B Bornkamp, H Seibold
Statistics in medicine 37 (10), 1608-1624, 2018
242018
Estimating patient-specific treatment advantages in the ‘Treatment for Adolescents with Depression Study’
S Foster, M Mohler-Kuo, L Tay, T Hothorn, H Seibold
Journal of psychiatric research 112, 61-70, 2019
222019
A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses
H Seibold, S Czerny, S Decke, R Dieterle, T Eder, S Fohr, N Hahn, ...
Plos one 16 (6), e0251194, 2021
182021
Survival forests under test: Impact of the proportional hazards assumption on prognostic and predictive forests for amyotrophic lateral sclerosis survival
N Korepanova, H Seibold, V Steffen, T Hothorn
Statistical Methods in Medical Research 29 (5), 1403-1419, 2020
142020
model4you: An R Package for Personalised Treatment Effect Estimation
H Seibold, A Zeileis, T Hothorn
Journal of Open Research Software 7 (1), 2019
142019
Package ‘partykit’
T Hothorn, H Seibold, A Zeileis, MT Hothorn
122023
Association between post-operative delirium and use of volatile anesthetics in the elderly: A real-world big data approach
T Saller, L Hubig, H Seibold, Z Schroeder, B Wang, P Groene, ...
Journal of clinical anesthesia 83, 110957, 2022
122022
What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?
S Dandl, T Hothorn, H Seibold, E Sverdrup, S Wager, A Zeileis
arXiv preprint arXiv:2206.10323, 2022
72022
Statisticians, roll up your sleeves! There's a crisis to be solved
H Seibold, A Charlton, AL Boulesteix, S Hoffmann
Significance 18 (4), 42-44, 2021
72021
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