The DOI matches its Crossref record; 32 of the 34 references it cites were found in a registry or an index.
Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints
Open accessAbstract
BACKGROUND: Modern modelling techniques may potentially provide more accurate predictions of binary outcomes than classical techniques. We aimed to study the predictive performance of different modelling techniques in relation to the effective sample size ("data hungriness"). METHODS: We performed simulation studies based on three clinical cohorts: 1282 patients with head and neck cancer (with 46.9% 5 year survival), 1731 patients with traumatic brain injury (22.3% 6 month mortality) and 3181 patients with minor head injury (7.6% with CT scan abnormalities). We compared three relatively modern modelling techniques: support vector machines (SVM), neural nets (NN), and random forests (RF) and two classical techniques: logistic regression (LR) and classification and regression trees (CART). We created three large artificial databases with 20 fold, 10 fold and 6 fold replication of subjects, where we generated dichotomous outcomes according to different underlying models. We applied each modelling technique to increasingly larger development parts (100 repetitions). The area under the ROC-curve (AUC) indicated the performance of each model in the development part and in an independent validation part. Data hungriness was defined by plateauing of AUC and small optimism (difference between the mean apparent AUC and the mean validated AUC <0.01). RESULTS: We found that a stable AUC was reached by LR at approximately 20 to 50 events per variable, followed by CART, SVM, NN and RF models. Optimism decreased with increasing sample sizes and the same ranking of techniques. The RF, SVM and NN models showed instability and a high optimism even with >200 events per variable. CONCLUSIONS: Modern modelling techniques such as SVM, NN and RF may need over 10 times as many events per variable to achieve a stable AUC and a small optimism than classical modelling techniques such as LR. This implies that such modern techniques should only be used in medical prediction problems if very large data sets are available.
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“The sample size must be several times larger if we want to use machine learning models.53”
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [53]
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Alavijeh, Z. S., & Borhani, M. (2026) · International Conference on Intelligent Systems and Artificial Intelligence Applications (ISAA) · doi:10.1109/isaa67392.2026.11726643
Machine learning to predict osteoradionecrosis in head and neck cancer
de Castro, V. T., Dalmazo, L. M. T., dos Reis, P. E. D., de Abreu, W. C., dos Reis, R. G., Prado-Ribeiro, A. C., Leite, A. F., Almeida, F. T., & Guerra, E. N. S. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1955800
Yohannes, E. T., Berhan, E., Mulugeta, A., & Kitaw, D. (2026) · SN Business & Economics · doi:10.1007/s43546-026-01519-5
Cui, P., Xu, S., Lin, X., Song, S., Huang, B., & Xu, Z. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1946352
Salı, M., Yi̇ği̇t, E., Ardıç, S. D., Sali, S., Çoşkun, A., Özçelik, E. E., Özmarasalı, B. E., Aktaş, A., Şahin, A. B., Çubukçu, E., et al. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15218262
Gültürk, E. A., & Güldoğan, E. (2026) · Konuralp Tıp Dergisi · doi:10.18521/ktd.1996338
Metadata
- Authors
- van der Ploeg, T., Austin, P. C., & Steyerberg, E. W.
- Affiliations
- University of Toronto (CA)
- Erasmus MC (NL)
- Venue
- BMC Medical Research Methodology
- Published
- 2014-12
- Volume
- 14
- Issue
- 1
- Pages
- 137-137
- ISSN
- 1471-2288 (ISSN-L)
- PMC
- PMC4289553
- OpenAlex
- W1602160603
- Cited by
- 809
- References
- 34
- Citations by year
- 2026: 118 · 2025: 107 · 2024: 91 · 2023: 117 · 2022: 114 · 2021: 107 · 2020: 59 · 2019: 39 · 2018: 30 · 2017: 13 · 2016: 8 · 2015: 6
- FWCI
- 9.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Research Methodology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- TSpace (University of Toronto) (submittedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- effective sample size, predictive performance, binary outcome prediction, support vector machine, artificial neural networks, random forest, logistic regression, classification and regression trees, optimism, traumatic brain injury mortality, mild traumatic brain injury, CT abnormalities, model stability
- MeSH terms
- Brain Injuries, Data Interpretation, Statistical, Head and Neck Neoplasms, Humans, ROC Curve, Models, Statistical, Databases, Factual, Head Injuries, Closed, Treatment Outcome, Endpoint Determination, Support Vector Machine
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