David Ohlssen

957 total citations
31 papers, 628 citations indexed

About

David Ohlssen is a scholar working on Statistics and Probability, Management Science and Operations Research and Economics and Econometrics. According to data from OpenAlex, David Ohlssen has authored 31 papers receiving a total of 628 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Statistics and Probability, 8 papers in Management Science and Operations Research and 7 papers in Economics and Econometrics. Recurrent topics in David Ohlssen's work include Statistical Methods in Clinical Trials (20 papers), Statistical Methods and Inference (8 papers) and Optimal Experimental Design Methods (8 papers). David Ohlssen is often cited by papers focused on Statistical Methods in Clinical Trials (20 papers), Statistical Methods and Inference (8 papers) and Optimal Experimental Design Methods (8 papers). David Ohlssen collaborates with scholars based in Switzerland, United States and United Kingdom. David Ohlssen's co-authors include David J. Spiegelhalter, Linda Sharples, Hayley E Jones, Heinz Schmidli, Beat Neuenschwander, Amy Racine, Michael Branson, Björn Bornkamp, Marc Vandemeulebroecke and Frank Bretz and has published in prestigious journals such as PLoS ONE, Neurology and Journal of Clinical Epidemiology.

In The Last Decade

David Ohlssen

31 papers receiving 613 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Ohlssen Switzerland 11 369 184 90 76 66 31 628
Telba Irony United States 15 222 0.6× 215 1.2× 39 0.4× 83 1.1× 57 0.9× 35 725
Babak Choodari‐Oskooei United Kingdom 13 360 1.0× 216 1.2× 24 0.3× 118 1.6× 96 1.5× 32 849
Mark Chang United States 12 441 1.2× 165 0.9× 30 0.3× 109 1.4× 179 2.7× 48 712
Ariel Alonso Belgium 18 676 1.8× 313 1.7× 68 0.8× 79 1.0× 130 2.0× 85 1.1k
Ying‐Qi Zhao United States 17 817 2.2× 219 1.2× 129 1.4× 21 0.3× 63 1.0× 70 1.3k
Bohdana Ratitch United States 13 406 1.1× 184 1.0× 33 0.4× 34 0.4× 25 0.4× 28 614
Michael O’Kelly United States 13 355 1.0× 180 1.0× 22 0.2× 48 0.6× 28 0.4× 27 548
Satrajit Roychoudhury United States 15 772 2.1× 283 1.5× 27 0.3× 110 1.4× 183 2.8× 38 1.1k
Grace Y. Yi Canada 18 673 1.8× 122 0.7× 163 1.8× 45 0.6× 44 0.7× 66 883
Laura Thompson United States 11 375 1.0× 174 0.9× 17 0.2× 71 0.9× 62 0.9× 34 706

Countries citing papers authored by David Ohlssen

Since Specialization
Citations

This map shows the geographic impact of David Ohlssen'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 David Ohlssen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Ohlssen more than expected).

Fields of papers citing papers by David Ohlssen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Ohlssen. 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 David Ohlssen. The network helps show where David Ohlssen may publish in the future.

Co-authorship network of co-authors of David Ohlssen

This figure shows the co-authorship network connecting the top 25 collaborators of David Ohlssen. A scholar is included among the top collaborators of David Ohlssen 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 David Ohlssen. David Ohlssen 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.
Monod, Mélodie, Peter Krusche, Qian Cao, et al.. (2024). TorchSurv: A Lightweight Package for Deep Survival Analysis. The Journal of Open Source Software. 9(104). 7341–7341. 3 indexed citations
2.
Baillie, Mark, et al.. (2024). All that Glitters Is not Gold: Type‐I Error Controlled Variable Selection from Clinical Trial Data. Clinical Pharmacology & Therapeutics. 115(4). 774–785. 2 indexed citations
3.
Sechidis, Konstantinos, Sophie Sun, Yao Chen, et al.. (2024). WATCH: A Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors. Pharmaceutical Statistics. 24(2). e2463–e2463. 1 indexed citations
4.
Baillie, Mark, et al.. (2022). Good Data Science Practice: Moving Toward a Code of Practice for Drug Development. Statistics in Biopharmaceutical Research. 15(1). 74–85. 4 indexed citations
5.
Sun, Sophie, Konstantinos Sechidis, Yao Chen, et al.. (2022). Comparing algorithms for characterizing treatment effect heterogeneity in randomized trials. Biometrical Journal. 66(1). e2100337–e2100337. 7 indexed citations
6.
Sechidis, Konstantinos, Matthías Kormáksson, & David Ohlssen. (2021). Using knockoffs for controlled predictive biomarker identification. Statistics in Medicine. 40(25). 5453–5473. 9 indexed citations
7.
Greenhouse, Joel B., et al.. (2020). Risk scoring for time to end-stage knee osteoarthritis: data from the Osteoarthritis Initiative. Osteoarthritis and Cartilage. 28(8). 1020–1029. 9 indexed citations
8.
Chen, Ji, David Ohlssen, & Yingchun Zhou. (2018). Functional Mixed Effects Model for the Analysis of Dose-Titration Studies. Statistics in Biopharmaceutical Research. 10(3). 176–184. 2 indexed citations
9.
Meier, Daniela Piani, et al.. (2017). Multiple Sclerosis Care Optimization Tool (MS-COT): A Clinical application prototype to predict future disease activity (P1.368). Neurology. 88(16_supplement). 1 indexed citations
10.
Gallo, Paul, et al.. (2017). On the Optimal Timing of Futility Interim Analyses. Statistics in Biopharmaceutical Research. 9(3). 293–301. 10 indexed citations
11.
Akacha, Mouna, Frank Bretz, David Ohlssen, G. Rosenkranz, & Heinz Schmidli. (2017). Estimands and Their Role in Clinical Trials. Statistics in Biopharmaceutical Research. 9(3). 268–271. 45 indexed citations
12.
Bornkamp, Björn, David Ohlssen, Baldur Magnusson, & Heinz Schmidli. (2016). Model averaging for treatment effect estimation in subgroups. Pharmaceutical Statistics. 16(2). 133–142. 17 indexed citations
13.
Weaver, Jerry, et al.. (2015). Strategies on Using Prior Information When Assessing Adverse Events. Statistics in Biopharmaceutical Research. 8(1). 106–115. 5 indexed citations
14.
Ohlssen, David & Amy Racine. (2014). A Flexible Bayesian Approach for Modeling Monotonic Dose–Response Relationships in Drug Development Trials. Journal of Biopharmaceutical Statistics. 25(1). 137–156. 4 indexed citations
15.
Armstrong, Linda, et al.. (2011). Safety of formoterol in adults and children with asthma: a meta-analysis. Annals of Allergy Asthma & Immunology. 107(1). 71–78. 4 indexed citations
16.
Jones, Hayley E, David Ohlssen, Beat Neuenschwander, Amy Racine, & Michael Branson. (2011). Bayesian models for subgroup analysis in clinical trials. Clinical Trials. 8(2). 129–143. 73 indexed citations
17.
Ohlssen, David. (2009). A Review of: “Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating, by E. W. Steyerberg”. Journal of Biopharmaceutical Statistics. 19(6). 1165–1167. 3 indexed citations
18.
Jones, Hayley E, David Ohlssen, & David J. Spiegelhalter. (2007). Use of the false discovery rate when comparing multiple health care providers. Journal of Clinical Epidemiology. 61(3). 232–240.e2. 72 indexed citations
19.
Suckling, John, David Ohlssen, Christopher Andrew, et al.. (2007). Components of variance in a multicentre functional MRI study and implications for calculation of statistical power. Human Brain Mapping. 29(10). 1111–1122. 35 indexed citations
20.
Ohlssen, David, Linda Sharples, & David J. Spiegelhalter. (2006). Flexible random‐effects models using Bayesian semi‐parametric models: applications to institutional comparisons. Statistics in Medicine. 26(9). 2088–2112. 120 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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