Maarten van Smeden

19.7k total citations · 11 hit papers
139 papers, 6.9k citations indexed

About

Maarten van Smeden is a scholar working on Statistics and Probability, Economics and Econometrics and Statistics, Probability and Uncertainty. According to data from OpenAlex, Maarten van Smeden has authored 139 papers receiving a total of 6.9k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Statistics and Probability, 31 papers in Economics and Econometrics and 27 papers in Statistics, Probability and Uncertainty. Recurrent topics in Maarten van Smeden's work include Health Systems, Economic Evaluations, Quality of Life (31 papers), Meta-analysis and systematic reviews (23 papers) and Statistical Methods in Clinical Trials (22 papers). Maarten van Smeden is often cited by papers focused on Health Systems, Economic Evaluations, Quality of Life (31 papers), Meta-analysis and systematic reviews (23 papers) and Statistical Methods in Clinical Trials (22 papers). Maarten van Smeden collaborates with scholars based in Netherlands, United Kingdom and United States. Maarten van Smeden's co-authors include Ben Van Calster, Johannes B. Reitsma, Gary S. Collins, Ewout W. Steyerberg, Richard D Riley, Karel G.M. Moons, Laure Wynants, Kym I E Snell, Joie Ensor and Karel G. M. Moons and has published in prestigious journals such as SHILAP Revista de lepidopterología, Annals of Internal Medicine and PLoS ONE.

In The Last Decade

Maarten van Smeden

126 papers receiving 6.8k citations

Hit Papers

Calculating the sample size required for develop... 2016 2026 2019 2022 2020 2019 2021 2018 2016 400 800 1.2k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Calculating the sample size required for developing a clinical prediction model
    2020 1.2k citations Richard D Riley, Johannes B. Reitsma et al. profile →
  2. Calibration: the Achilles heel of predictive analytics
    2019 910 citations Ben Van Calster, Maarten van Smeden et al. profile →
  3. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence
    2021 475 citations Gary S. Collins, Paula Dhiman et al. BMJ Open profile →
  4. Sample size for binary logistic prediction models: Beyond events per variable criteria
    2018 372 citations Maarten van Smeden, Karel G. M. Moons et al. profile →
  5. No rationale for 1 variable per 10 events criterion for binary logistic regression analysis
    2016 323 citations Maarten van Smeden, Karel G. M. Moons et al. profile →
  6. Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review
    2022 279 citations Anne de Hond, Artuur Leeuwenberg et al. profile →
  7. Minimum sample size for external validation of a clinical prediction model with a binary outcome
    2021 186 citations Richard D Riley, Gary S. Collins et al. Statistics in Medicine profile →
  8. Evaluation of clinical prediction models (part 1): from development to external validation
    2024 183 citations Gary S. Collins, Paula Dhiman et al. profile →
  9. The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression
    2022 146 citations Maarten van Smeden, D. Timmerman et al. profile →
  10. There is no such thing as a validated prediction model
    2023 117 citations Ben Van Calster, Laure Wynants et al. profile →
  11. Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study
    2024 68 citations Richard D Riley, Ben Van Calster et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maarten van Smeden Netherlands 37 1.2k 1.1k 939 931 771 139 6.9k
Thomas P. A. Debray Netherlands 40 1.1k 0.9× 735 0.7× 951 1.0× 1.1k 1.1× 512 0.7× 108 6.4k
Marcus Eng Hock Ong Singapore 47 1.1k 0.9× 688 0.6× 1.2k 1.2× 1.2k 1.3× 395 0.5× 500 8.8k
Kym I E Snell United Kingdom 27 979 0.8× 562 0.5× 1.1k 1.1× 847 0.9× 447 0.6× 73 5.3k
Benjamin A. Goldstein United States 39 836 0.7× 740 0.7× 805 0.9× 996 1.1× 263 0.3× 163 5.9k
Joie Ensor United Kingdom 25 931 0.8× 503 0.5× 963 1.0× 841 0.9× 398 0.5× 66 5.1k
Robert Wolff United Kingdom 27 1.1k 0.8× 326 0.3× 1.0k 1.1× 499 0.5× 590 0.8× 96 6.6k
Lucas M. Bachmann Switzerland 47 1.4k 1.2× 551 0.5× 1.8k 1.9× 1.1k 1.2× 2.6k 3.4× 229 11.3k
Hardeep Singh United States 63 897 0.7× 584 0.5× 1.1k 1.2× 407 0.4× 1.7k 2.1× 376 13.2k
Matthew M. Churpek United States 43 3.1k 2.5× 1.0k 1.0× 1.4k 1.5× 790 0.8× 265 0.3× 188 6.6k
Niels Peek United Kingdom 42 927 0.7× 679 0.6× 389 0.4× 1.1k 1.2× 286 0.4× 251 5.8k

Countries citing papers authored by Maarten van Smeden

Since Specialization
Citations

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

Fields of papers citing papers by Maarten van Smeden

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maarten van Smeden. 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 Maarten van Smeden. The network helps show where Maarten van Smeden may publish in the future.

Co-authorship network of co-authors of Maarten van Smeden

This figure shows the co-authorship network connecting the top 25 collaborators of Maarten van Smeden. A scholar is included among the top collaborators of Maarten van Smeden 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 Maarten van Smeden. Maarten van Smeden 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.
Venekamp, Roderick P, Anne de Hond, Esther de Groot, et al.. (2025). Development and internal validation of a diagnostic prediction model for life-threatening events in callers with shortness of breath: a cross-sectional study in out-of-hours primary care. British Journal of General Practice. 75(756). e500–e508.
2.
Smeden, Maarten van, Petra J. M. Elders, Jan Festen, et al.. (2025). Charlson comorbidity index has no incremental value for mortality risk prediction in nursing home residents with COVID-19 disease. BMC Geriatrics. 25(1). 67–67.
3.
Kaul, T. K., Johanna AAG Damen, Laure Wynants, et al.. (2025). Assessing the quality of prediction models in health care using the Prediction model Risk Of Bias ASsessment Tool (PROBAST): an evaluation of its use and practical application. Journal of Clinical Epidemiology. 181. 111732–111732. 1 indexed citations
4.
Kaul, T. K., Johanna AAG Damen, Ewoud Schuit, et al.. (2024). Methodological quality assessment tools for diagnosis and prognosis research: overview and guidance. Journal of Clinical Epidemiology. 177. 111609–111609. 1 indexed citations
5.
Platteel, Tamara N, Geert‐Jan Geersing, Paul Little, et al.. (2024). Prognostic factors and prediction models for hospitalisation and all-cause mortality in adults presenting to primary care with a lower respiratory tract infection: a systematic review. BMJ Open. 14(3). e075475–e075475. 1 indexed citations
6.
Sushil, Madhumita, Atul J. Butte, Ewoud Schuit, Maarten van Smeden, & Artuur Leeuwenberg. (2024). Cross-institution natural language processing for reliable clinical association studies: a methodological exploration. Journal of Clinical Epidemiology. 167. 111258–111258. 3 indexed citations
7.
Bond, Marinde J. G., Maarten van Smeden, Koen Degeling, et al.. (2024). Predicting Benefit From FOLFOXIRI Plus Bevacizumab in Patients With Metastatic Colorectal Cancer. JCO Clinical Cancer Informatics. 8(8). e2400037–e2400037. 2 indexed citations
8.
Calster, Ben Van, et al.. (2024). A comparison of hyperparameter tuning procedures for clinical prediction models: A simulation study. Statistics in Medicine. 43(6). 1119–1134. 9 indexed citations
9.
Damen, Johanna AAG, Banafsheh Arshi, Maarten van Smeden, et al.. (2024). Validation of prognostic models predicting mortality or ICU admission in patients with COVID-19 in low- and middle-income countries: a global individual participant data meta-analysis. SHILAP Revista de lepidopterología. 8(1). 17–17. 2 indexed citations
10.
Vickers, Andrew J., Melissa Assel, Rodney L. Dunn, et al.. (2023). Guidelines for reporting observational research in urology: the importance of clear reference to causality. British Journal of Urology. 132(1). 4–8. 3 indexed citations
11.
12.
Offerhaus, Joost A., Maarten van Smeden, Marijke Linschoten, et al.. (2022). Sex- and age specific association of new-onset atrial fibrillation with in-hospital mortality in hospitalised COVID-19 patients. IJC Heart & Vasculature. 39. 100970–100970. 10 indexed citations
13.
Navarro, Constanza L. Andaur, Johanna AAG Damen, Maarten van Smeden, et al.. (2022). Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models. Journal of Clinical Epidemiology. 154. 8–22. 58 indexed citations
14.
Royen, Florien S van, Karel G.M. Moons, Geert‐Jan Geersing, & Maarten van Smeden. (2022). Developing, validating, updating and judging the impact of prognostic models for respiratory diseases. European Respiratory Journal. 60(3). 2200250–2200250. 22 indexed citations
15.
Damen, Johanna AAG, Karel G.M. Moons, Maarten van Smeden, & Lotty Hooft. (2022). How to conduct a systematic review and meta-analysis of prognostic model studies. Clinical Microbiology and Infection. 29(4). 434–440. 51 indexed citations
16.
Haller, Maria, Constantin Aschauer, Christine Wallisch, et al.. (2022). Prediction models for living organ transplantation are poorly developed, reported, and validated: a systematic review. Journal of Clinical Epidemiology. 145. 126–135. 7 indexed citations
17.
Royen, Florien S van, et al.. (2021). COVID-19-isolatiespreekuren tijdens de ‘eerste golf’. Huisarts en Wetenschap. 1–7. 1 indexed citations
18.
Zwart, Dorien, Esther de Groot, Maarten van Smeden, et al.. (2021). Gender-stratified analyses of symptoms associated with acute coronary syndrome in telephone triage: a cross-sectional study. BMJ Open. 11(6). e042406–e042406. 9 indexed citations
19.
Schweitzer, Valentijn A., Cornelis H. van Werkhoven, Jesús Rodríguez‐Baño, et al.. (2019). Optimizing design of research to evaluate antibiotic stewardship interventions: consensus recommendations of a multinational working group. Clinical Microbiology and Infection. 26(1). 41–50. 53 indexed citations
20.
Brink, Michel S., Bionka M.A. Huisstede, Maarten van Smeden, et al.. (2018). Does a bounding exercise program prevent hamstring injuries in adult male soccer players? – A cluster‐RCT. Scandinavian Journal of Medicine and Science in Sports. 29(4). 515–523. 35 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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