David S. Robertson

713 total citations
28 papers, 235 citations indexed

About

David S. Robertson is a scholar working on Statistics and Probability, Management Science and Operations Research and Economics and Econometrics. According to data from OpenAlex, David S. Robertson has authored 28 papers receiving a total of 235 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Statistics and Probability, 10 papers in Management Science and Operations Research and 7 papers in Economics and Econometrics. Recurrent topics in David S. Robertson's work include Statistical Methods in Clinical Trials (16 papers), Optimal Experimental Design Methods (9 papers) and Health Systems, Economic Evaluations, Quality of Life (7 papers). David S. Robertson is often cited by papers focused on Statistical Methods in Clinical Trials (16 papers), Optimal Experimental Design Methods (9 papers) and Health Systems, Economic Evaluations, Quality of Life (7 papers). David S. Robertson collaborates with scholars based in United Kingdom, Germany and Switzerland. David S. Robertson's co-authors include James Wason, Sofía S. Villar, A Toby Prevost, Jack Bowden, Thomas Jaki, Laura Flight, Munyaradzi Dimairo, Philip Pallmann, Babak Choodari‐Oskooei and Claire Davis and has published in prestigious journals such as Bioinformatics, PLoS Pathogens and Statistics in Medicine.

In The Last Decade

David S. Robertson

23 papers receiving 227 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
David S. Robertson United Kingdom	10	145	66	47	25	21	28	235
Xinran Li United States	11	279 1.9×	39 0.6×	23 0.5×	9 0.4×	37 1.8×	39	450
Caroline Morgan Australia	10	107 0.7×	44 0.7×	59 1.3×	39 1.6×		13	260
Roel Braekers Belgium	9	120 0.8×	18 0.3×	40 0.9×	6 0.2×		31	292
Youssef El‐Khatib United Arab Emirates	10	43 0.3×	20 0.3×	91 1.9×	11 0.4×	3 0.1×	31	306
Matteo Cellamare United States	8	55 0.4×	30 0.5×	46 1.0×	9 0.4×		23	232
Sandeep Menon United States	10	98 0.7×	27 0.4×	37 0.8×	22 0.9×		36	315
Nicolas Savy France	12	81 0.6×	19 0.3×	52 1.1×	8 0.3×	1 0.0×	29	274
Cristina Sotto Belgium	7	225 1.6×	33 0.5×	29 0.6×	18 0.7×	1 0.0×	11	312
Jitendra Ganju United States	10	101 0.7×	72 1.1×	23 0.5×	33 1.3×		30	349

Countries citing papers authored by David S. Robertson

Since Specialization

Citations

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

Fields of papers citing papers by David S. Robertson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David S. Robertson

This figure shows the co-authorship network connecting the top 25 collaborators of David S. Robertson. A scholar is included among the top collaborators of David S. Robertson 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 S. Robertson. David S. Robertson is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Wolff, Daniel, Corey Cutler, Thomas Jaki, et al.. (2025). Toward Better and More Effective Clinical Trials for Chronic Graft-Versus-Host Disease. Transplantation and Cellular Therapy.

Noor, Nurulamin M, Haiyan Zheng, David S. Robertson, & Siddharth Singh. (2025). Revolutionizing IBD Clinical Trials with Bayesian Approaches. Inflammatory Bowel Diseases. 31(12). 3481–3483.

Robertson, David S., Thomas Burnett, Babak Choodari‐Oskooei, et al.. (2025). Confidence Intervals for Adaptive Trial Designs I: A Methodological Review. Statistics in Medicine. 44(18-19). e70174–e70174.

Villar, Sofía S., et al.. (2024). How could a pooled testing policy have performed in managing the early stages of the COVID‐19 pandemic? Results from a simulation study. Statistics in Medicine. 43(11). 2239–2262.

Robertson, David S., Thomas J Burnett, Babak Choodari‐Oskooei, et al.. (2024). Confidence intervals for adaptive trial designs I: A methodological review. arXiv (Cornell University).

Glimm, Ekkehard & David S. Robertson. (2023). Familywise error rate control for block response-adaptive randomization. Statistical Methods in Medical Research. 32(6). 1193–1202. 1 indexed citations

Robertson, David S., Babak Choodari‐Oskooei, Munyaradzi Dimairo, et al.. (2023). Point estimation for adaptive trial designs II: Practical considerations and guidance. Statistics in Medicine. 42(14). 2496–2520. 9 indexed citations

Robertson, David S., James Wason, Franz König, Martin Posch, & Thomas Jaki. (2023). Online error rate control for platform trials. Statistics in Medicine. 42(14). 2475–2495. 9 indexed citations

Robertson, David S., et al.. (2023). Response-Adaptive Randomization in Clinical Trials: From Myths to Practical Considerations. Statistical Science. 38(2). 185–208. 41 indexed citations

10.

Evans, Stephanie, et al.. (2023). Evaluating pooled testing for asymptomatic screening of healthcare workers in hospitals. BMC Infectious Diseases. 23(1). 900–900. 3 indexed citations

11.

Robertson, David S., et al.. (2022). The benefits of covariate adjustment for adaptive multi-arm designs. Statistical Methods in Medical Research. 31(11). 2104–2121. 4 indexed citations

12.

Kunzmann, Kevin, et al.. (2022). Conditional power and friends: The why and how of (un)planned, unblinded sample size recalculations in confirmatory trials. Statistics in Medicine. 41(5). 877–890. 8 indexed citations

13.

Robertson, David S., Babak Choodari‐Oskooei, Munyaradzi Dimairo, et al.. (2022). Point estimation for adaptive trial designs I: A methodological review. Statistics in Medicine. 42(2). 122–145. 28 indexed citations

14.

Kunzmann, Kevin, et al.. (2021). A Review of Bayesian Perspectives on Sample Size Derivation for Confirmatory Trials. The American Statistician. 75(4). 424–432. 9 indexed citations

15.

Wason, James & David S. Robertson. (2020). Controlling type I error rates in multi‐arm clinical trials: A case for the false discovery rate. Pharmaceutical Statistics. 20(1). 109–116. 21 indexed citations

16.

Robertson, David S. & James Wason. (2018). Familywise error control in multi-armed response-adaptive trials. Newcastle University ePrints (Newcastle Univesity). 7 indexed citations

17.

Robertson, David S., A Toby Prevost, & Jack Bowden. (2016). Accounting for selection and correlation in the analysis of two-stage genome-wide association studies. Biostatistics. 17(4). 634–649. 9 indexed citations

18.

Leoz, Marie, Felix Feyertag, Anfumbom Kfutwah, et al.. (2015). The Two-Phase Emergence of Non Pandemic HIV-1 Group O in Cameroon. PLoS Pathogens. 11(8). e1005029–e1005029. 13 indexed citations

19.

Davis, Claire, et al.. (2014). Reduced length fibre Bragg gratings for high frequency acoustic sensing. Measurement Science and Technology. 25(12). 125105–125105. 21 indexed citations

20.

Robertson, David S., et al.. (2004). Geographic Information Systems, Charts and UNCLOS – Can they live together?. Maritime Studies. 2004(136). 1–6. 3 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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