Cited by 666Statistical Methods in EpidemiologyStatistical Methods and InferenceStatistical Methods in Clinical Trials

The DOI matches its Crossref record; 55 of the 56 references it cites were found in a registry or an index.

Sample size considerations for the external validation of a multivariable prognostic model: a resampling study

Open access

Collins, Gary S., Ogundimu, Emmanuel O., Altman, Douglas G.·2016·Statistics in Medicine

Open this paper in CoChat — free account Open PDF

Abstract

After developing a prognostic model, it is essential to evaluate the performance of the model in samples independent from those used to develop the model, which is often referred to as external validation. However, despite its importance, very little is known about the sample size requirements for conducting an external validation. Using a large real data set and resampling methods, we investigate the impact of sample size on the performance of six published prognostic models. Focussing on unbiased and precise estimation of performance measures (e.g. the c ‐index, D statistic and calibration), we provide guidance on sample size for investigators designing an external validation study. Our study suggests that externally validating a prognostic model requires a minimum of 100 events and ideally 200 (or more) events. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1002/sim.6787

Cites

55 of 55 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1993) · doi:10.1007/978-1-4899-4541-9 · Cited by 21,230
Evaluation of Factors Related to Late Recurrence - Later than 10 Years after the Initial Treatment - in Primary Breast Cancer
Nishimura, R., Osako, T., Nishiyama, Y., Tashima, R., Nakano, M., Fujisue, M., Toyozumi, Y., & Arima, N. (2013) · Oncology · doi:10.1159/000353099 · Cited by 13,239
Regression Modeling Strategies
Harrell, F. E. (2001) · Springer series in statistics · doi:10.1007/978-1-4757-3462-1 · Cited by 9,270
General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study
D’Agostino, R. B., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M., & Kannel, W. B. (2008) · Circulation · doi:10.1161/circulationaha.107.699579 · Cited by 7,491
Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis
Harrell (2015) · Springer Series in Statistics · doi:10.1007/978-3-319-19425-7 · Cited by 6,878
Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures
Steyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M. J., & Kattan, M. W. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181c30fb2 · Cited by 5,170
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration
Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147
Self-citation
Evaluating the Yield of Medical Tests
Harrell, F. E. (1982) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.1982.03320430047030 · Cited by 3,490
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement
Collins, P. G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · British Journal of Cancer · doi:10.1038/bjc.2014.639 · Cited by 3,319
Self-citation

Flagged first, then by citations

Show all 56 references

1 of the 56 references couldn’t be matched to a known paper.

Sorted by citations

Items 601–666 of 666

Rapid screening of depression and anxiety in outpatients: Symptom-based nomograms for early identification from gastrointestinal presentations
Zhong, L., Xu, Z., Liu, X., Wang, D., Tong, Q.-Y., Tian, X., Xiong, J., Hou, X., & Bai, T. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.120580
Sample size determination for external validation of risk models with binary outcomes using the area under the ROC curve
Zhou, Y., Zheng, Y., Li, C., Sharma, A., & Tang, L. (2025) · Epidemiologic Methods · doi:10.1515/em-2025-0012
Safety and effectiveness of the Kaiser Permanente early-onset neonatal sepsis calculator in Qatar
Vellamgot, A. P., Salim, S. T., Salameh, K., Thazhe, S. B. K., Elikkottil, A., Habboub, L., Pattuvalappil, R., & Joseph, B. E. (2025) · Frontiers in Pediatrics · doi:10.3389/fped.2025.1637914
Toward transparent and rigorous validation of a prediction score
Yen, C. (2025) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2025.108250
Authors’ reply “Toward transparent and rigorous validation of a prediction score”
Liu, R., Ru, X., & Ye, Q. (2025) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2025.108251
Additional file 4 of Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review
Senent‐Valero, M., Librero, J., & Pastor‐Valero, M. (2021) · Figshare · doi:10.6084/m9.figshare.17132896
Additional file 3 of Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data
Yang, C. B., Fridgeirsson, E. A., Kors, J. A., Reps, J. M., & Rijnbeek, P. R. (2024) · Figshare · doi:10.6084/m9.figshare.26660464
Additional file 1 of External validation of 5A score model for predicting in-hospital mortality among the accidental hypothermia patients: JAAM-Hypothermia study 2018–2019 secondary analysis
OKADA, Y., Matsuyama, T., Hayashida, K., Takauji, S., Kanda, J., & Yokobori, S. (2022) · Figshare · doi:10.6084/m9.figshare.19905856
Additional file 1 of [18F]FDG-PET/CT-based risk stratification in women with locally advanced uterine cervical cancer
Adam, J. A., Poel, E., van Eck-Smit, B. L., Mom, C. H., Stalpers, L. J. A., Laan, J. J., Kidd, E. A., Stoker, J., & Bipat, S. (2024) · Figshare · doi:10.6084/m9.figshare.25678529
Additional file 1 of Development and validation of new poisoning mortality score system for patients with acute poisoning at the emergency department
Han, K. S., Kim, S. J., Lee, E. J., Shin, J. H., Lee, J. S., & Lee, S. W. (2021) · Figshare · doi:10.6084/m9.figshare.13605569
Additional file 5 of Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data
Yang, C. B., Fridgeirsson, E. A., Kors, J. A., Reps, J. M., & Rijnbeek, P. R. (2024) · Figshare · doi:10.6084/m9.figshare.26660470
Additional file 1 of Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data
Yang, C. B., Fridgeirsson, E. A., Kors, J. A., Reps, J. M., & Rijnbeek, P. R. (2024) · Figshare · doi:10.6084/m9.figshare.24940484
Additional file 6 of Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data
Yang, C. B., Fridgeirsson, E. A., Kors, J. A., Reps, J. M., & Rijnbeek, P. R. (2024) · Figshare · doi:10.6084/m9.figshare.26660473
Additional file 2 of Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review
Senent‐Valero, M., Librero, J., & Pastor‐Valero, M. (2021) · Figshare · doi:10.6084/m9.figshare.17132887
Additional file 2 of Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data
Yang, C. B., Fridgeirsson, E. A., Kors, J. A., Reps, J. M., & Rijnbeek, P. R. (2024) · Figshare · doi:10.6084/m9.figshare.24940986
Additional file 2 of Quantitative prediction error analysis to investigate predictive performance under predictor measurement heterogeneity at model implementation
Luijken, K., Song, J., & Groenwold, R. H. H. (2022) · Figshare · doi:10.6084/m9.figshare.19533915
Additional file 1 of Clinical prediction models for diagnosis of COVID-19 among adult patients: a validation and agreement study
Dardenne, N., Locquet, M., Diep, A. N., Gilbert, A., Delrez, S., Beaudart, C., Brabant, C., Ghuysen, A., Donneau, A.-F., & Bruyère, O. (2022) · Figshare · doi:10.6084/m9.figshare.19767815
Additional file 3 of Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review
Senent‐Valero, M., Librero, J., & Pastor‐Valero, M. (2021) · Figshare · doi:10.6084/m9.figshare.17132893
Additional file 1 of Predicting neurological outcome in adult patients with cardiac arrest: systematic review and meta-analysis of prediction model performance
Amacher, S. A., Blatter, R., Briel, M., Appenzeller‐Herzog, C., Bohren, C., Becker, C., Beck, K., Gross, S., Tisljar, K., Sutter, R., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26552957
Additional file 4 of Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data
Yang, C. B., Fridgeirsson, E. A., Kors, J. A., Reps, J. M., & Rijnbeek, P. R. (2024) · Figshare · doi:10.6084/m9.figshare.26660467
Additional file 1 of Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network
Williams, R. D., Markus, A. F., Yang, C. B., Duarte‐Salles, T., DuVall, S., Falconer, T., Jonnagaddala, J., Kim, C., Rho, Y., Williams, A. E., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19094134
Additional file 1 of Validation of the REduction of Atherothrombosis for Continued Health (REACH) prediction model for recurrent cardiovascular disease among United Arab Emirates Nationals
Al-Shamsi, S., & Govender, R. D. (2020) · Figshare · doi:10.6084/m9.figshare.13115153
Additional file 1 of Advancing polytrauma care: developing and validating machine learning models for early mortality prediction
He, W., Fu, X., & Chen, S. (2024) · Figshare · doi:10.6084/m9.figshare.26619469
Additional file 1 of Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review
Senent‐Valero, M., Librero, J., & Pastor‐Valero, M. (2021) · Figshare · doi:10.6084/m9.figshare.17132884
Additional file 1 of Development and validation of an equation to predict the incidence of coronary heart disease in patients with type 2 diabetes in Japan
Yamashita, Y., Inoue, G., Nozaki, Y., Kitajima, R., Matsubara, K., Horii, T., Mohri, J., Atsuda, K., & Matsubara, H. (2021) · Figshare · doi:10.6084/m9.figshare.17083637
Additional file 1 of Quantitative prediction error analysis to investigate predictive performance under predictor measurement heterogeneity at model implementation
Luijken, K., Song, J., & Groenwold, R. H. H. (2022) · Figshare · doi:10.6084/m9.figshare.19533909
Modeling and validation of serum miR-18a and miR-122 levels as predictors of recurrence after laparoscopic radical cystectomy procedure for bladder cancer based on nomogram model
Wang, P., Deng, J., & Wu, S. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2025.1579873
Development and External Validation of the SANTANDER Score: A Primary Care Clinical Decision Tool for Cardiovascular Risk Stratification in ColombiaPreprint
Therán-León, J. S., García, C. F., Pinzón, H. T., Strauch-Díaz, C. I., & Arenas-Moreno, C. E. (2026) · Research Square · doi:10.21203/rs.3.rs-8674390/v1
Early risk assessment of severe outcomes in patients with suspected infection in the emergency department using clinical scores in a multicenter international validation
Hincapié, C., van der Aart, T. J., van Wijk, R. J., Föhse, K., Hoogerwerf, J. J., Jaimes, F., Koeze, J., Linder, A., van Londen, M., ter Maaten, J. C., et al. (2026) · Journal of Infection and Public Health · doi:10.1016/j.jiph.2026.103170
Calibration and discrimination ability of the Dat'AIDS score in people living with HIV aged 70 years and older from the Dat'AIDS cohort
Mousse, A. H., Allavena, C., Gagneux‐Brunon, A., Raffi, F., Hocqueloux, L., Genet, C., Pascard, M., Jolly, D., Bani‐Sadr, F., Kanagaratnam, L., et al. (2026) · HIV Medicine · doi:10.1111/hiv.70207
External validation of risk indices and the development of a multivariable risk prediction model for surgical site infection in adults: a prospective observational study
Troeman, D. P. R., Harbarth, S., Kluytmans, J. A. J. W., van Werkhoven, C. H., the ASPIRE-SSI Study Team, Malhotra‐Kumar, S., Timbermont, L., Vlaeminck, J., Vilken, T., Xavier, B. B., et al. (2026) · Antimicrobial Resistance and Infection Control · doi:10.1186/s13756-026-01712-z
Explainable machine learning prediction of tracheostomy after craniotomy for supratentorial intracerebral hemorrhage
Qiao, F., Xue, X., Yu, H., Cai, Y., Tian, D., Wang, Y., Liu, Q., & Li, Q. (2026) · Scientific Reports · doi:10.1038/s41598-026-41953-x
Recurrence risk prediction model for hepatitis B virus-associated hepatocellular carcinoma patients: a systematic review and meta-analysis
Zhao, K.-H., Liu, J., Chen, Y.-S., Yi, W.-T., Liu, J.-J., & Zeng, Y. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1777061
Development and validation of an intraoperative hypothermia risk assessment scale for school-aged children
Du, Q., Wang, Z., Yang, L., Liu, Y. H., Cao, J., Xia, M., & Yang, X. (2026) · Frontiers in Pediatrics · doi:10.3389/fped.2026.1770475
Performances pronostiques du FibroTest chez les sujets avec maladie métabolique du foie
Munteanu, M. (2018) · doi:10.70675/215a8b02zdac2z4a71z802bz1c60de192f4d
Protocol for a randomized trial to predict the efficacy of cognitive and behavioral interventions for symptoms of depression
Ding, J., Chiu, J. C., Moon, S., Ren, Y., Turner, D., Shoval, G., Niv, Y., & Berwian, I. M. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2026.1774560
A novel approach to the assessment of repeat self-harm in prisoners: development and validation of a tool to bridge assessment and treatment (RAPSS)
Fazel, S., Heathcote, L., Farouki, L., Senior, J., Perry, A., Fanshawe, T. R., & Shaw, J. J. (2025) · Health Technology Assessment · doi:10.3310/gjsf1605
Development and Validation of an Early Prediction Model for Postoperative Gastrointestinal Bleeding After Cardiac Surgery Using the MIMIC-IV Database
Li, X., Shen, Q., Liu, Z., Zhai, Q., & Zhang, X. (2026) · The Heart Surgery Forum · doi:10.31083/hsf50244
Validating an Oropharyngeal Cancer Risk Prediction Model with Data from a Canadian Population
Ghanati, H., Nicolau, B. F., Ying, Y., Koushik, A., & Madathil, S. (2026) · Journal of The Canadian Dental Association · doi:10.66515/jcda.92.q1
Self‐harm in individuals with substance use disorders: Predictive factors and risk model
Yu, R., Tracy, D. K., Sinclair, J., Brikell, I., Lichtenstein, P., Larsson, H., & Fazel, S. (2026) · Addiction · doi:10.1111/add.70465
External Evaluation Reveals Reduced Performance of the PathFX Survival Model in 1018 Patients With Extremity Metastases
de Groot, T. M., Gonzalez, M. R., van der Linden, L. R., Skornja, I., Groot, O. Q., Schwab, J. H., Doornberg, J. N., Jutte, P. C., & Lozano‐Calderón, S. A. (2026) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000003955 · Cited by 1
A National Prospective Multicenter Comparison of Risk Scores Against Clinical Judgement for the Prediction of Safe Discharge From the Emergency Department in Patients Presenting With Lower Gastrointestinal Bleeding
Sey, M. S. L., Renteln, D. A. V., Kohansal, A., van Zanten, S. V., Singh, H., Edmonds, M., Wiemer, H., Zhou, F., Pedicelli, A., Keshlaf, S., et al. (2026) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2026.05.014
External Validation of the SCARE Score for Predicting Acute Myocardial Infarction in Patients Calling Emergency Medical Communication Centers: A Prospective Multicenter Study
Garrouste, V., Guérineau, A., Guénézan, J., Moumneh, T., Narcisse, S., Martin, P. L., Giovannetti, O., Bouvier, F., & Jouffroy, R. (2026) · Prehospital Emergency Care · doi:10.1080/10903127.2026.2677853
Enhancing coastal public welfare through government support, service access, local institutions, and local economic development
Eryanto, H., Marsofiyati, M., Adha, M. A., Manik, T., Ali, Y., & Sumardi, N. A. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.103054
Use of the Appendistat (APS) score to triage patients with suspected acute appendicitis: a retrospective cross-sectional study in one of the busiest UK surgical emergency units
Parry-Jones, T., Kostopoulos, G., Fragkos, K. C., Cohen, R., Roberts, G. P., Bond‐Smith, G., & Eddama, M. M. R. (2026) · International Journal of Surgery · doi:10.1097/js9.0000000000005433
Subgroup deployment-readiness audit of calibration and clinical utility in perioperative infection prediction
Guillen-Ramirez, H., Lucas, K. L., Wintsch, Y. M., Blatter, T. U., Triep, K., Endrich, O., & Beldi, G. (2026) · Journal of the American Medical Informatics Association · doi:10.64898/2026.06.15.26355656
External validation of the VALE scoring system for hemorrhage risk in pediatric AVM patients
Kashefiolasl, S., Porto, L., Lachner, K., Merker, M., Schrewe, R., Schubert‐Bast, S., Keil, F., Prinz, V., Finger, T., & Czabanka, M. (2026) · Neurosurgical Review · doi:10.1007/s10143-026-04366-y
Integrating genetic, epigenetic, and clinical signatures via machine learning for robust prediction of leflunomide response in rheumatoid arthritis: a multi-center validation study
Chen, M. Y., Liu, H., Jin, L., Feng, X., Dai, B., Wang, F., Wang, Q., Chen, Y., Yi, M., Jia, B., et al. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1804485
Externally validated risk prediction models for gestational diabetes mellitus: A systematic review and meta‐analysis
Seifu, B. L., Belsti, Y., Yimer, N. B., Goldstein, R. F., Enticott, J., & Teede, H. J. (2026) · Acta Obstetricia Et Gynecologica Scandinavica · doi:10.1111/aogs.70300
External validation of a nomogram for de novo bone metastasis in breast cancer: a single-center Mexican cohort
Canizalez‐Román, A., Llanes-Cardenas, R., Martínez‐Villa, F. A., & Izabal, D. Y. A. (2026) · Clinical & Translational Oncology · doi:10.1007/s12094-026-04490-7
A multicentre evaluation of cardiovascular risk prediction tools in people living with HIV across England ( ECARTH study)
Lee, M. J., Murray, A., Smith, C. J., Abdi, Z., Duncan, A., Dwyer, E., Gompels, M., Henderson, M., Johnson, M. A., Lander, F., et al. (2026) · HIV Medicine · doi:10.1111/hiv.70282
Pharmacogenomics of antipsychotic-induced weight gain: A systematic review
Kronenbuerger, M., Yoshida, K., Li, P. Y., Tavakoli, E., Magarbeh, L., Elsheikh, S. S. M., Gorbovskaya, I., Zai, C. C. H., Fleischmann, R., Zai, G., et al. (2026) · Schizophrenia Research · doi:10.1016/j.schres.2026.07.013
Outcome Prediction After [ 177 Lu]Lu-PSMA I&T in Metastatic Castration-Resistant Prostate Cancer: External Validation of a [ 68 Ga]Ga-PSMA-11 PET–Based Model with [ 18 F]F-Flotufolastat
Karimzadeh, A., Fütterer, C., Hansen, K., Coban, Ö., Hein, S., Nekolla, S. G., Heck, M., Tauber, R., Haller, B., Gafita, A., et al. (2026) · Journal of Nuclear Medicine · doi:10.2967/jnumed.125.270944
Exploring prognostic factors in breast cancer: development and selection of optimal machine learning models
Shen, M., Wang, Y., Wu, Z., Chen, L., Wu, S., Pan, H., & Xu, B. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1830769
FTIR spectroscopy-based differentiation of seborrheic dermatitis and scalp psoriasis using clinically normal fingernails
Liu, R., Peng, B., Qi, K., Zhang, M., & Kuang, Y. (2026) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2026.128518
Development and evaluation of the electronic frailty index+ (eFI+) tool for older people: prognostic prediction modelling with integrated decision curve and health economic analysis
Archer, L., Relton, S. D., Akbari, A., Best, K., Bestwick, R., Bucknall, M., Conroy, S., Hattle, M., Hollinghurst, J., Howdon, D., et al. (2025) · Health Technology Assessment · doi:10.3310/gjac1008
NeuroSpectNet: A Novel Architecture for Early Stage Detection of Executive Dysfunction From Resting-State EEG
Rieck, C., Eisentraut, L., Penava, P., & Buettner, R. (2026) · IEEE Access · doi:10.1109/access.2026.3723687
Evaluation of the SMS-ICU score for multicontinental use: A federated external validation study using critical care registry data
Tracy, A., Fazla, F., Salluh, J. I. F., Haniffa, R., Uddin, R. A. M. E., Aryal, D., Brinkman, S., Burghi, G., Doherty, Z. B., Dongelmans, D. A., et al. (2026) · Critical Care and Resuscitation · doi:10.1016/j.ccrj.2026.100206
Development and Independent Validation of a Machine Learning–Based Noninvasive Venous Waveform Analysis for Heart Failure Device to Estimate Pulmonary Capillary Wedge Pressure
Alvis, B. D., Schmeckpeper, J., Rali, A. S., Huston, J., Tsai, S., Amancherla, K. V., Armstrong, D., Gupta, R., Mede, A. I., Whitfield, J. S., et al. (2026) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2026.08.010
External Validation of the Time to Walking Independently After Stroke (TWIST) Prediction Algorithm within 72 Hours Poststroke
Rajkovic, L. A., Veerbeek-Preuss, S., Bärtschi, S., Ottiger, B., Nyffeler, T., & Veerbeek, J. M. (2026) · Neurorehabilitation and neural repair · doi:10.1177/15459683261477077
Validation of the discriminatory performance of the iPREFACE score for clinically significant fetal acidemia: an outcome-enriched nested case–control study
Ito, A., Kamiya, M., Hayata, E., Nagasaki, S., & Nakata, M. (2026) · AJOG Global Reports · doi:10.1016/j.xagr.2026.100694
Prognostic value of 48-h reassessment of a laboratory-derived frailty index in ICU patients with sepsis: model development in MIMIC-IV and external transport evaluation
He, M., Zhang, Y., Xiao, J., Yang, M., Wen, Y., & Huang, D. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1926236
Pathological Response Depth and Pre-Treatment Risk Stratification After Neoadjuvant Chemoradiotherapy for Rectal Cancer
Negru, T., Stana, L. G., Stanga, L., Dejeu, D., Ilie, A. C., & Pantea, Ș. (2026) · Medical Sciences · doi:10.3390/medsci14060632
A preoperative risk-scoring model for predicting intraoperative hemorrhage during endoscopic submucosal dissection for gastric neoplasms
Wang, Y.-P., Wang, W., Chen, J., Luo, J.-D., Xuan, J., He, X., Liu, W.-M., Wei, X.-J., Wu, Q.-Y., Zhang, Y.-X., et al. (2026) · Gastric Cancer · doi:10.1007/s10120-026-01806-y
Cross-Platform External Validation of a Locked 12-Gene Prognostic Panel in Lung Adenocarcinoma Benchmarked Against Pathological Stage
Alrumaihi, F. (2026) · Genes · doi:10.3390/genes17101264
Enhancing diagnostic and prognostic prediction of child psychopathology via multivariate predictive modeling: Where are we and where do we need to go?
Karalunas, S. L., Faraone, S. V., Mooney, M. A., Weiskopf, N. G., Doyle, A. E., Kurtz‐Nelson, E. C., Keehn, R. M., Sibley, M. H., Wozniak, J. R., Zaback, T., et al. (2026) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2026.10.006

Authors: Collins, G. S., Ogundimu, E. O., & Altman, D. G.
Affiliations: Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre University of Oxford Windmill Road Oxford OX3 7LD U.K.
University of Oxford (GB)
ORCID: Collins, Gary S.
Ogundimu, Emmanuel O.
Altman, Douglas G.
Venue: Statistics in Medicine
Publisher: Wiley
Published: 2015-11-09
Volume: 35
Issue: 2
Pages: 214-226
ISSN: 0277-6715 (ISSN-L)
1097-0258
DOI: 10.1002/sim.6787
PMC: PMC4738418
OpenAlex: W2122892545
Cited by: 665
References: 56
Citations by year: 2026: 51 · 2025: 87 · 2024: 77 · 2023: 60 · 2022: 89 · 2021: 90 · 2020: 74 · 2019: 51 · 2018: 38 · 2017: 24 · 2016: 23
FWCI: 25.28
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
doi.wiley.com/10.1002/tdm_license_1.1 (tdm)
Locations: Statistics in Medicine (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Durham Research Online (Durham University) (publishedVersion, other-oa)
Durham Research Online (Durham University) (acceptedVersion, other-oa)
Durham Research Online (Durham University) (submittedVersion, cc-by)
Northumbria Research Link (Northumbria University) (submittedVersion)
University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
Funders: Medical Research Council (G1100513)
Medical Research Council Prognosis Research Strategy (PROGRESS) Partnership (G0902393/99558)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords: external validation, sample size, resampling, concordance index, calibration, D-statistic, event counts, model performance estimation
MeSH terms: Cardiovascular Diseases, Diabetes Mellitus, Type 2, Humans, Prognosis, Risk Factors, Models, Statistical, Multivariate Analysis, Databases, Factual, Sample Size, Validation Studies as Topic, Biostatistics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Sample size considerations for the external validation of a multivariable prognostic model: a resampling study

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks