Guido Skipka

1.2k total citations
28 papers, 880 citations indexed

About

Guido Skipka is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Molecular Biology. According to data from OpenAlex, Guido Skipka has authored 28 papers receiving a total of 880 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Statistics and Probability, 6 papers in Statistics, Probability and Uncertainty and 4 papers in Molecular Biology. Recurrent topics in Guido Skipka's work include Statistical Methods in Clinical Trials (10 papers), Meta-analysis and systematic reviews (6 papers) and Statistical Methods and Bayesian Inference (3 papers). Guido Skipka is often cited by papers focused on Statistical Methods in Clinical Trials (10 papers), Meta-analysis and systematic reviews (6 papers) and Statistical Methods and Bayesian Inference (3 papers). Guido Skipka collaborates with scholars based in Germany, United States and Netherlands. Guido Skipka's co-authors include Ralf Bender, Oliver Kuß, Anne Catharina Brockhaus, Tim Friede, Peter Schlattmann, Guido Schwarzer, Armin Koch, Jochen Zange, Matthias Vorgerd and Lüdger Schöls and has published in prestigious journals such as Pain, Journal of Clinical Epidemiology and Clinical Chemistry.

In The Last Decade

Guido Skipka

28 papers receiving 858 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Guido Skipka Germany 18 153 134 102 94 89 28 880
Jennifer S. Gewandter United States 24 227 1.5× 360 2.7× 49 0.5× 212 2.3× 103 1.2× 91 1.8k
Joseph Stauffer United States 13 133 0.9× 171 1.3× 37 0.4× 97 1.0× 17 0.2× 27 1.1k
Svjetlana Došenović Croatia 15 125 0.8× 112 0.8× 23 0.2× 143 1.5× 115 1.3× 38 811
W Król United States 12 62 0.4× 49 0.4× 192 1.9× 241 2.6× 19 0.2× 22 1.3k
Hisashi Urushihara Japan 15 125 0.8× 83 0.6× 77 0.8× 107 1.1× 10 0.1× 73 828
Christopher O’Connor United States 21 154 1.0× 167 1.2× 50 0.5× 446 4.7× 7 0.1× 73 2.1k
P E Leaverton United States 14 57 0.4× 143 1.1× 42 0.4× 111 1.2× 23 0.3× 22 957
S Beard United Kingdom 19 75 0.5× 81 0.6× 33 0.3× 276 2.9× 11 0.1× 39 1.3k
Gillian C. Hall United Kingdom 20 201 1.3× 342 2.6× 26 0.3× 252 2.7× 9 0.1× 48 1.8k
Paul Farquhar-Smith United Kingdom 15 67 0.4× 266 2.0× 85 0.8× 180 1.9× 12 0.1× 35 1.1k

Countries citing papers authored by Guido Skipka

Since Specialization
Citations

This map shows the geographic impact of Guido Skipka's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Guido Skipka with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guido Skipka more than expected).

Fields of papers citing papers by Guido Skipka

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Guido Skipka. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Guido Skipka. The network helps show where Guido Skipka may publish in the future.

Co-authorship network of co-authors of Guido Skipka

This figure shows the co-authorship network connecting the top 25 collaborators of Guido Skipka. A scholar is included among the top collaborators of Guido Skipka based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Guido Skipka. Guido Skipka is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Felsch, Moritz, Lars Beckmann, Ralf Bender, et al.. (2022). Performance of several types of beta-binomial models in comparison to standard approaches for meta-analyses with very few studies. BMC Medical Research Methodology. 22(1). 319–319. 8 indexed citations
2.
Schürmann, Christoph, et al.. (2021). Performing Meta-analyses with Very Few Studies. Methods in molecular biology. 2345. 91–102. 20 indexed citations
3.
Bender, Ralf, Tim Friede, Armin Koch, et al.. (2018). Methods for evidence synthesis in the case of very few studies. Research Synthesis Methods. 9(3). 382–392. 154 indexed citations
4.
Grouven, Ulrich, et al.. (2012). A note on the graphical presentation of prediction intervals in random-effects meta-analyses. Systematic Reviews. 1(1). 34–34. 48 indexed citations
5.
Kasper, Jürgen, et al.. (2011). Population-Based Screening of Children for Specific Speech and Language Impairment in Germany: A Systematic Review. Folia Phoniatrica et Logopaedica. 63(5). 247–263. 11 indexed citations
6.
Kromp, Mandy, et al.. (2011). Reporting of loss to follow-up information in randomised controlled trials with time-to-event outcomes: a literature survey. BMC Medical Research Methodology. 11(1). 130–130. 24 indexed citations
7.
8.
Bender, Ralf & Guido Skipka. (2010). Intervention Effects in the Case of Heterogeneity between Three Subgroups. Methods of Information in Medicine. 49(6). 613–617. 3 indexed citations
9.
Bender, Ralf, Armin Koch, Guido Skipka, Thomas Kaiser, & Stefan Lange. (2010). The assessment of heterogeneity is mandatory in clinical trials and systematic reviews. Journal of Clinical Epidemiology. 64(4). 452–452. 1 indexed citations
10.
Follmann, Markus, et al.. (2007). Developing search strategies for clinical practice guidelines in SUMSearch and Google Scholar and assessing their retrieval performance. BMC Medical Research Methodology. 7(1). 28–28. 28 indexed citations
11.
Schöls, Lüdger, Jochen Zange, Michael Abele, et al.. (2004). L-carnitine and creatine in Friedreich’s ataxia. A randomized, placebo-controlled crossover trial. Journal of Neural Transmission. 112(6). 789–796. 45 indexed citations
12.
Endres, Heinz G., M. Zenz, C. Schaub, et al.. (2004). Zur Problematik von Akupunkturstudien am Beispiel der Methodik von gerac. Der Schmerz. 19(3). 201–213. 20 indexed citations
13.
Munk, Axel, Guido Skipka, & Bernd O. Stratmann. (2004). Testing general hypotheses under binomial sampling: the two sample case—asymptotic theory and exact procedures. Computational Statistics & Data Analysis. 49(3). 723–739. 1 indexed citations
14.
Maier, Christoph, Roman Dertwinkel, Ingolf Hosbach, et al.. (2003). Efficacy of the NMDA-receptor antagonist memantine in patients with chronic phantom limb pain – results of a randomized double-blinded, placebo-controlled trial. Pain. 103(3). 277–283. 104 indexed citations
15.
Müller, Annette, et al.. (2002). Expression of CD34 in Pulmonary Endothelial Cells in vivo<sup>1</sup>. Pathobiology. 70(1). 11–17. 10 indexed citations
16.
Müller, Annette, et al.. (2002). Expression of von Willebrand Factor by Human Pulmonary Endothelial Cells in vivo<footref rid="foot01"><sup>1</sup></footref>. Respiration. 69(6). 526–533. 26 indexed citations
17.
Müller, Annette, et al.. (2002). Correlation of age with in vivo expression of endothelial markers. Experimental Gerontology. 37(5). 713–719. 23 indexed citations
18.
Meiser, Andreas, et al.. (2001). Quantifizierung von Blutverlusten. Der Anaesthesist. 50(1). 13–20. 61 indexed citations
19.
Stachon, Axel, Andreas Böning, M. Krismann, et al.. (2001). Prognostic Significance of the Presence of Erythroblasts in Blood after Cardiothoracic Surgery. Clinical Chemistry and Laboratory Medicine (CCLM). 39(3). 239–43. 20 indexed citations
20.
Schöls, Lüdger, et al.. (2001). Idebenone in patients with Friedreich ataxia. Neuroscience Letters. 306(3). 169–172. 60 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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