output
20102026
most citedAutomated Design of Deep Learning Methods for Biomedical Image Segmentation

9.8k citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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5 papers · 1 filter

stat.ME2023★ 60 cited

metamedian: An R package for meta-analyzing studies reporting medians

Sean McGrath, XiaoFei Zhao, Omer Ozturk +3

When performing an aggregate data meta-analysis of a continuous outcome, researchers often come across primary studies that report the sample median of the outcome. However, standa…

stat.ME2022★ 13 cited

Perpetual Observational Studies: New strategies to support efficient implementation of observational studies and randomized trials in the infectious diseases arena

N. Hassoun-Kheir, C. H. van Werkhoven, J. Dunning +17

The increasing threat of emerging infectious diseases and antimicrobial resistance requires more efficient, high-quality research. Perpetual Observational Studies (POS) nested with…

stat.ME2022★ 49 cited

Standard error estimation in meta-analysis of studies reporting medians

Sean McGrath, Stephan Katzenschlager, Alexandra J. Zimmer +3

We consider the setting of an aggregate data meta-analysis of a continuous outcome of interest. When the distribution of the outcome is skewed, it is often the case that some prima…

stat.ME2020★ 4 cited

Measurement Error in Meta-Analysis (MEMA) -- a Bayesian framework for continuous outcome data

Harlan Campbell, Valentijn M. T. de Jong, Lauren Maxwell +3

Ideally, a meta-analysis will summarize data from several unbiased studies. Here we consider the less than ideal situation in which contributing studies may be compromised by measu…

stat.ME2018★ 156 cited

Likelihood-based meta-analysis with few studies: Empirical and simulation studies

Svenja E. Seide, Christian Röver, Tim Friede

Standard random-effects meta-analysis methods perform poorly when applied to few studies only. Such settings however are commonly encountered in practice. It is unclear, whether or…