Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes
Open accessAbstract
When designing a study to develop a new prediction model with binary or time‐to‐event outcomes, researchers should ensure their sample size is adequate in terms of the number of participants ( n ) and outcome events ( E ) relative to the number of predictor parameters ( p ) considered for inclusion. We propose that the minimum values of n and E (and subsequently the minimum number of events per predictor parameter, EPP) should be calculated to meet the following three criteria: (i) small optimism in predictor effect estimates as defined by a global shrinkage factor of ≥ 0.9, (ii) small absolute difference of ≤ 0.05 in the model's apparent and adjusted Nagelkerke's R 2 , and (iii) precise estimation of the overall risk in the population. Criteria (i) and (ii) aim to reduce overfitting conditional on a chosen p , and require prespecification of the model's anticipated Cox‐Snell R 2 , which we show can be obtained from previous studies. The values of n and E that meet all three criteria provides the minimum sample size required for model development. Upon application of our approach, a new diagnostic model for Chagas disease requires an EPP of at least 4.8 and a new prognostic model for recurrent venous thromboembolism requires an EPP of at least 23. This reinforces why rules of thumb (eg, 10 EPP) should be avoided. Researchers might additionally ensure the sample size gives precise estimates of key predictor effects; this is especially important when key categorical predictors have few events in some categories, as this may substantially increase the numbers required.
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The model development lifecycle includes internal validation, external testing, calibration assessment, and monitoring to detect performance drift over time.
“The model development lifecycle includes internal validation, external testing, calibration assessment, and monitoring to detect performance drift over time [43].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [43]
The section on prespecifying R² discusses external validation as a way to estimate performance in new individuals, and the Discussion mentions calibration in new data. The paper focuses on sample-size criteria for model development; the passages do not present the claimed lifecycle or address monitoring for performance drift over time. Page numbers were not available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAdequate sample size is important for prediction model development, especially when outcomes are rare or many predictors are used, because insufficient sample size increases overfitting risk.
“Adequate sample size is important for prediction model development, especially when outcomes are rare or many predictors are used, because insufficient sample size increases overfitting risk [43].”
Reliable detection of moderate molecular effects in Alzheimer's disease requires at least 500 paired omics and imaging samples, yet most studies fall short of this benchmark.
“For instance, reliable detection of moderate molecular effects in AD requires at least 500 paired omics and imaging samples, yet most studies fall short of this benchmark423.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [423]
The section on precise estimates of predictor effects says sample-size needs depend on the effect and study characteristics; its worked example concerns venous thromboembolism, not Alzheimer’s disease. Searches for Alzheimer’s disease, paired omics and imaging, and a 500-sample threshold found no passage supporting that benchmark or the assertion that most studies fall short. (LLM, conf=0.77)
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Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works
Bootstrap methods: another look at the jackknife
Efron, B. (1979) · The Annals of Statistics · doi:10.1214/aos/1176344552 · Cited by 17,602 · see citing works
A simulation study of the number of events per variable in logistic regression analysis
Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(96)00236-3 · Cited by 9,655 · see citing works
Cragg, J. G., & Zarembka, P. (1975) · Canadian Journal of Economics/Revue canadienne d économique · doi:10.2307/134075 · Cited by 8,385 · see citing works
Harrell (2015) · Springer Series in Statistics · doi:10.1007/978-3-319-19425-7 · Cited by 6,878 · see citing works
A note on a general definition of the coefficient of determination
NAGELKERKE, N. J. D. (1991) · Biometrika · doi:10.1093/biomet/78.3.691 · Cited by 5,927 · see citing works
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 · see citing works
Self-citationRelaxing the Rule of Ten Events per Variable in Logistic and Cox Regression
Vittinghoff, E., & McCulloch, C. E. (2007) · American Journal of Epidemiology · doi:10.1093/aje/kwk052 · Cited by 4,359 · see citing works
Cardiovascular disease risk profiles
Anderson, K. M., Odell, P. M., Wilson, P. W. F., & Kannel, W. B. (1991) · American Heart Journal · doi:10.1016/0002-8703(91)90861-b · Cited by 2,163 · see citing works
Peduzzi, P., Concato, J., Feinstein, A. R., & Holford, T. R. (1995) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(95)00048-8 · Cited by 2,154 · see citing works
Anderson, D., Rodger, M., Ginsberg, J., Kearon, C., Gent, M., Turpie, A., Bormanis, J., Weitz, J., Chamberlain, M., Bowie, D., et al. (2000) · Thrombosis and Haemostasis · doi:10.1055/s-0037-1613830 · Cited by 1,651 · see citing works
Royston, P. J., & Parmar, M. (2002) · Statistics in Medicine · doi:10.1002/sim.1203 · Cited by 1,537 · see citing works
Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research
Steyerberg, E. W., Moons, K. G. M., van der Windt, D. A., Hayden, J. A., Perel, P., Schroter, S., Riley, R. D., Hemingway, H., Altman, D. G., & for the PROGRESS Group (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001381 · Cited by 1,508 · see citing works
Self-citationSteyerberg, E. W. (2009) · Statistics for Biology and Health · doi:10.1007/978-0-387-77244-8 · Cited by 1,491 · see citing works
Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2
Hippisley-Cox, J., Coupland, C., Vinogradova, Y., Robson, J., Minhas, R., Sheikh, A., & Brindle, P. (2008) · BMJ · doi:10.1136/bmj.39609.449676.25 · Cited by 1,459 · cited 5× · see citing works
“1 Using the LR statistic to derive the Cox-Snell Radj If the R(2)CS_app or R 2 CS_adj is not available in the publication of an existing model, the LR value may be reported, which would allow R(2)CS_app to be derived using Equation (6), then SVH for the model derived using Equation (7) (assuming the model's n and p ar…”
BackgroundMethodInfluentialDvoretzky, A., Kiefer, J., & Wolfowitz, J. (1956) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177728174 · Cited by 1,236 · see citing works
Value of assessment of pretest probability of deep-vein thrombosis in clinical management
Wells, P. S., Anderson, D. R., Bormanis, J., Guy, F., Mitchell, M. J., Gray, L. C., Clement, C., Robinson, K. S., & Lewandowski, B. J. (1997) · The Lancet · doi:10.1016/s0140-6736(97)08140-3 · Cited by 1,209 · see citing works
Prognosis and prognostic research: Developing a prognostic model
Royston, P., Moons, K. G. M., Altman, D. G., & Vergouwe, Y. (2009) · BMJ · doi:10.1136/bmj.b604 · Cited by 1,172 · see citing works
Self-citationThe Nottingham prognostic index in primary breast cancer
Galea, M. H., Blamey, R. W., Elston, C. W., & Ellis, I. O. (1992) · Breast Cancer Research and Treatment · doi:10.1007/bf01840834 · Cited by 1,071 · see citing works
Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research
Riley, R. D., Hayden, J. A., Steyerberg, E. W., Moons, K. G. M., Abrams, K., Kyzas, P. A., Malats, N., Briggs, A., Schroter, S., Altman, D. G., et al. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001380 · Cited by 823 · see citing works
Self-citationModern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints
van der Ploeg, T., Austin, P. C., & Steyerberg, E. W. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-137 · Cited by 809 · see citing works
Concato, J., Peduzzi, P. N., Holford, T. R., & Feinstein, A. R. (1995) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(95)00510-2 · Cited by 797 · see citing works
Sample size for binary logistic prediction models: Beyond events per variable criteria
van Smeden, M., Moons, K., de Groot, J. D., Collins, G., Altman, D., Eijkemans, M., & Reitsma, J. (2018) · Statistical Methods in Medical Research · doi:10.1177/0962280218784726 · Cited by 776 · see citing works
Self-citationA prognostic index in primary breast cancer.
Haybittle, J., Blamey, R., Elston, C., Johnson, J., Doyle, P., Campbell, F., Nicholson, R. I., & Griffiths, K. (1982) · British Journal of Cancer · doi:10.1038/bjc.1982.62 · Cited by 758 · see citing works
How to develop a more accurate risk prediction model when there are few events
Pavlou, M., Ambler, G., Seaman, S. R., Guttmann, O. P., Elliot, P. M., King, M., & Omar, R. (2015) · BMJ · doi:10.1136/bmj.h3868 · Cited by 668 · see citing works
A guide to systematic review and meta-analysis of prediction model performance
Debray, T. P. A., Damen, J. A. A. G., Snell, K. I. E., Ensor, J., Hooft, L., Reitsma, J. B., Riley, R. D., & Moons, K. G. M. (2017) · BMJ · doi:10.1136/bmj.i6460 · Cited by 657 · see citing works
Self-citationPredictive value of statistical models.
Van Houwelingen, J. C., & Le Cessie, S. (1990) · Statistics in Medicine · doi:10.1002/sim.4780091109 · Cited by 621 · see citing works
Regression, Prediction and Shrinkage
Copas, J. B. (1983) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1983.tb01258.x · Cited by 612 · see citing works
No rationale for 1 variable per 10 events criterion for binary logistic regression analysis
van Smeden, M., de Groot, J. D., Moons, K., Collins, G., Altman, D., Eijkemans, M., & Reitsma, J. (2016) · BMC Medical Research Methodology · doi:10.1186/s12874-016-0267-3 · Cited by 563 · see citing works
Self-citationAdequate sample size for developing prediction models is not simply related to events per variable
Ogundimu, E. O., Altman, D. G., & Collins, G. S. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2016.02.031 · Cited by 546 · see citing works
Sample Size Planning for Statistical Power and Accuracy in Parameter Estimation
Maxwell, S. E., Kelley, K., & Rausch, J. R. (2008) · Annual Review of Psychology · doi:10.1146/annurev.psych.59.103006.093735 · Cited by 542 · see citing works
Austin, P. C., & Steyerberg, E. W. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280214558972 · Cited by 516 · see citing works
A new measure of prognostic separation in survival data
Royston, P., & Sauerbrei, W. (2004) · Statistics in Medicine · doi:10.1002/sim.1621 · Cited by 450 · see citing works
R 2 Measures Based on Wald and Likelihood Ratio Joint Significance Tests
Magee, L. (1990) · The American Statistician · doi:10.1080/00031305.1990.10475731 · Cited by 439 · see citing works
Sample-Size Calculations for the Cox Proportional Hazards Regression Model with Nonbinary Covariates
Hsieh, F. Y., & Lavori, P. W. (2000) · Controlled Clinical Trials · doi:10.1016/s0197-2456(00)00104-5 · Cited by 374 · see citing works
Minimum sample size for developing a multivariable prediction model: Part I – Continuous outcomes
Riley, R. D., Snell, K. I. E., Ensor, J., Burke, D. L., Harrell, F. E., Moons, K. G. M., & Collins, P. G. S. (2018) · Statistics in Medicine · doi:10.1002/sim.7993 · Cited by 372 · see citing works
Self-citationExplained Variation for Survival Models
Royston, P. (2006) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x0600600105 · Cited by 319 · see citing works
Courvoisier, D. S., Combescure, C., Agoritsas, T., Gayet‐Ageron, A., & Perneger, T. (2011) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2010.11.012 · Cited by 318 · see citing works
R 2 Measures Based on Wald and Likelihood Ratio Joint Significance Tests
Magee, L. (1990) · The American Statistician · doi:10.2307/2685352 · Cited by 278 · see citing works
Hippisley‐Cox, J., & Coupland, C. A. C. (2017) · BMJ · doi:10.1136/bmj.j5019 · Cited by 218 · see citing works
Wynants, L., Bouwmeester, W., Moons, K. G. M., Moerbeek, M., Timmerman, D., Huffel, S. V., CALSTER, B. V., & Vergouwe, Y. (2015) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.02.002 · Cited by 178 · see citing works
Self-citationMoons, K. G. M., Donders, A. R. T., Steyerberg, E. W., & Harrell, F. E. (2004) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2004.01.020 · Cited by 175 · see citing works
Self-citationAn Overview of Variance Inflation Factors for Sample-Size Calculation
Hsieh, F. Y., Lavori, P. W., Cohen, H. J., & Feussner, J. R. (2003) · Evaluation & the Health Professions · doi:10.1177/0163278703255230 · Cited by 173 · see citing works
Explained randomness in proportional hazards models
O’Quigley, J., Xu, R., & Stare, J. (2004) · Statistics in Medicine · doi:10.1002/sim.1946 · Cited by 152 · see citing works
Using regression models for prediction: shrinkage and regression to the mean
Copas, J. B. (1997) · Statistical Methods in Medical Research · doi:10.1177/096228029700600206 · Cited by 128 · see citing works
Sultan, A. A., West, J., Grainge, M. J., Riley, R. D., Tata, L. J., Stephansson, O., Fleming, K. M., Nelson‐Piercy, C., & Ludvigsson, J. F. (2016) · BMJ · doi:10.1136/bmj.i6253 · Cited by 128 · see citing works
Self-citationShrinkage and Penalized Likelihood as Methods to Improve Predictive Accuracy
van Houwelingen, H. C. (2001) · Statistica Neerlandica · doi:10.1111/1467-9574.00154 · Cited by 86 · see citing works
Feiveson, A. H. (2002) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x0200200201 · Cited by 72 · see citing works
Tufts PACE Clinical Predictive Model Registry: update 1990 through 2015
Wessler, B. S., Paulus, J. K., Lundquist, C. M., Ajlan, M., Natto, Z. S., Janes, W., Jethmalani, N., Raman, G., Lutz, J. S., & Kent, D. M. (2017) · Diagnostic and Prognostic Research · doi:10.1186/s41512-017-0021-2 · Cited by 57 · see citing works
Predicting Mortality in Patients with Diabetes Starting Dialysis
van Diepen, M., Schroijen, M. A., Dekkers, O. M., Rotmans, J. I., Krediet, R. T., Boeschoten, E. W., & Dekker, F. W. (2014) · PLoS ONE · doi:10.1371/journal.pone.0089744 · Cited by 41 · see citing works
Ensor, J., Riley, R. D., Jowett, S. M., Monahan, M., Snell, K. I. E., Bayliss, S. E., Moore, D., Fitzmaurice, D. A., & on behalf of the PIT-STOP collaborative group (2016) · Health Technology Assessment · doi:10.3310/hta20120 · Cited by 40 · see citing works
Self-citationJinks, R. C., Royston, P., & Parmar, M. K. (2015) · BMC Medical Research Methodology · doi:10.1186/s12874-015-0078-y · Cited by 32 · see citing works
Poppe, K., Doughty, R. N., Wells, S., Gentles, D., Hemingway, H., Jackson, R., & Kerr, A. (2017) · Heart · doi:10.1136/heartjnl-2016-310668 · Cited by 29 · see citing works
Planning for precision in survival studies
Borenstein, M. (1994) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(94)90133-3 · Cited by 17 · see citing works
Sample size for binary logistic prediction models: Beyond events per variable criteria
van Smeden, M., Moons, K. G. M., de Groot, J. A. H., Collins, G. S., Altman, D. G., Eijkemans, M. J. C., & Reitsma, J. B. (2019) · Sage Journals Data · doi:10.25384/sage.c.4638293 · Cited by 11 · see citing works
Self-citationdo Brasil, P. E. A. A., Xavier, S. S., de Holanda, M. T., Hasslocher‐Moreno, A. M., & Braga, J. U. (2016) · Revista da Sociedade Brasileira de Medicina Tropical · doi:10.1590/0037-8682-0196-2016 · Cited by 7 · see citing works
Hendry, D. F., & Nielsen, B. J. (2012) · Princeton University Press eBooks · doi:10.2307/j.ctv30pnvtj · Cited by 5 · see citing works
Flagged first, then by citations
2 of the 61 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- [33]Mittlboeck M, Heinzl H. Pseudo R‐squared measures of generalized linear models. In: Proceedings of the 1st European Workshop on the Assessment of Diagnostic Performance; 2004; Milan, Italy.
- [49]Royston P, Lambert PC. Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model. Boca Raton, Fl: CRC Press; 2011.
Cited by
Sorted by citations
Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., et al. (2024) · BMJ · doi:10.1136/bmj-2023-078378 · Cited by 4,718 · see citing works
“…of TRIPOD 2015, there have been numerous methodological advances in prediction modelling, including sample size guidance for developing models 23-27 and evaluating their performance, 28-32 and greater recognition of operationalising fairness, 33 reproducibility, 34 and adopting open science…”
BackgroundSelf-citationCalculating the sample size required for developing a clinical prediction model
Riley, R. D., Ensor, J., Snell, K. I. E., Harrell, F. E., Martin, G. P., Reitsma, J. B., Moons, K. G. M., Collins, G., & van Smeden, M. (2020) · BMJ · doi:10.1136/bmj.m441 · Cited by 2,917 · see citing works
Self-citationCalibration: the Achilles heel of predictive analytics
On behalf of Topic Group ‘Evaluating diagnostic tests and prediction models’ of the STRATOS initiative, Van Calster, B., McLernon, D. J., van Smeden, M., Wynants, L., & Steyerberg, E. W. (2019) · BMC Medicine · doi:10.1186/s12916-019-1466-7 · Cited by 2,650 · see citing works
Moons, K. G. M., Wolff, R. F., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., & Mallett, S. (2019) · Annals of Internal Medicine · doi:10.7326/m18-1377 · Cited by 1,669 · see citing works
Self-citationRadiomics in medical imaging—“how-to” guide and critical reflection
van Timmeren, J. E., Cester, D., Tanadini-Lang, S., Alkadhi, H., & Baessler, B. (2020) · Insights into Imaging · doi:10.1186/s13244-020-00887-2 · Cited by 1,316 · see citing works
Moons, K. G. M., Damen, J. A. A., Kaul, T., Hooft, L., Andaur Navarro, C., Dhiman, P., Beam, A. L., Van Calster, B., Celi, L. A., Denaxas, S., et al. (2025) · BMJ · doi:10.1136/bmj-2024-082505 · Cited by 1,069 · see citing works
Self-citationExternal validation of prognostic models: what, why, how, when and where?
Ramspek, C. L., Jager, K. J., Dekker, F. W., Zoccali, C., & van Diepen, M. (2021) · Clinical Kidney Journal · doi:10.1093/ckj/sfaa188 · Cited by 887 · see citing works
Evaluation of clinical prediction models (part 1): from development to external validation
Collins, G. S., Dhiman, P., Ma, J., Schlussel, M. M., Archer, L., Van Calster, B., Harrell, F. E., Martin, G. P., Moons, K. G. M., van Smeden, M., et al. (2024) · BMJ · doi:10.1136/bmj-2023-074819 · Cited by 798 · see citing works
Self-citationEvaluation of clinical prediction models (part 2): how to undertake an external validation study
Riley, R., Archer, L., Snell, K., Ensor, J., Dhiman, P., Martin, G. P., Bonnett, L., & Collins, G. S. (2024) · British medical journal · doi:10.1136/bmj-2023-074820 · Cited by 515 · see citing works
Self-citation(2024) · BMJ · doi:10.1136/bmj.q902 · Cited by 416 · see citing works
“…of TRIPOD 2015, there have been numerous methodological advances in prediction modelling, including sample size guidance for developing models 23-27 and evaluating their performance, 28-32 and greater recognition of operationalising fairness, 33 reproducibility, 34 and adopting open science…”
Minimum sample size for external validation of a clinical prediction model with a binary outcome
Riley, R. D., Debray, T. P. A., Collins, G. S., Archer, L., Ensor, J., van Smeden, M., & Snell, K. I. E. (2021) · Statistics in Medicine · doi:10.1002/sim.9025 · Cited by 400 · see citing works
Self-citationGerry, S., Bonnici, T., Birks, J., Kirtley, S., Virdee, P. S., Watkinson, P., & Collins, P. G. S. (2020) · BMJ · doi:10.1136/bmj.m1501 · Cited by 398 · see citing works
Minimum sample size for developing a multivariable prediction model: Part I – Continuous outcomes
Riley, R. D., Snell, K. I. E., Ensor, J., Burke, D. L., Harrell, F. E., Moons, K. G. M., & Collins, P. G. S. (2018) · Statistics in Medicine · doi:10.1002/sim.7993 · Cited by 372 · see citing works
Self-citationHerrera, D., Sanz, M., Shapira, L., Brotons, C., Chapple, I., Frese, T., Graziani, F., Hobbs, R. F., Huck, O., Hummers, E., et al. (2023) · Journal Of Clinical Periodontology · doi:10.1111/jcpe.13807 · Cited by 315 · see citing works
Leisman, D. E., Harhay, M. O., Lederer, D. J., Abramson, M., Adjei, A. A., Bakker, J., Ballas, Z. K., Barreiro, E., Bell, S. C., Bellomo, R., et al. (2020) · Critical Care Medicine · doi:10.1097/ccm.0000000000004246 · Cited by 271 · see citing works
Mansournia, M. A., Collins, P. G. S., Nielsen, R. O., Nazemipour, M., Jewell, N. P., Altman, D. G., & Campbell, M. J. (2021) · British Journal of Sports Medicine · doi:10.1136/bjsports-2020-103652 · Cited by 268 · see citing works
DNA methylation-based predictors of health: applications and statistical considerations
Yousefi, P., Suderman, M. J., Langdon, R., Whitehurst, O., Smith, G. D., & Relton, C. L. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00465-w · Cited by 228 · see citing works
Criteria for the translation of radiomics into clinically useful tests
Huang, E. P., O’Connor, J. P. B., McShane, L. M., Giger, M. L., Lambin, P., Kinahan, P. E., Siegel, E. L., & Shankar, L. K. (2022) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-022-00707-0 · Cited by 244 · see citing works
Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome
Cervia, C., Zurbuchen, Y., Taeschler, P., Ballouz, T., Menges, D., Hasler, S., Adamo, S., Raeber, M. E., Bächli, E., Rudiger, A., et al. (2022) · Nature Communications · doi:10.1038/s41467-021-27797-1 · Cited by 222 · see citing works
Meehan, A. J., Lewis, S. J., Fazel, S., Fusar-Poli, P., Steyerberg, E. W., Stahl, D., & Danese, A. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01528-4 · Cited by 219 · see citing works
Riley, R. D., Snell, K. I. E., Archer, L., Ensor, J., Debray, T. P. A., CALSTER, B. V., van Smeden, M., & Collins, P. G. S. (2024) · BMJ · doi:10.1136/bmj-2023-074821 · Cited by 213 · see citing works
Self-citationMinimum sample size for external validation of a clinical prediction model with a continuous outcome
Archer, L., Snell, K. I. E., Ensor, J., Hudda, M. T., Collins, G. S., & Riley, R. D. (2021) · Statistics in Medicine · doi:10.1002/sim.8766 · Cited by 198 · see citing works
Self-citationStability of clinical prediction models developed using statistical or machine learning methods
Riley, R. D., & Collins, G. S. (2023) · Biometrical Journal · doi:10.1002/bimj.202200302 · Cited by 211 · see citing works
Self-citationA Scoping Review of Machine Learning in Psychotherapy Research
Aafjes-van Doorn, K., Kamsteeg, C., Bate, J., & Aafjes, M. (2021) · Psychotherapy Research · doi:10.1080/10503307.2020.1808729 · Cited by 179 · see citing works
Toward a unified framework for interpreting machine-learning models in neuroimaging
Kohoutová, L., Heo, J., Cha, S., Lee, S., Moon, T., Wager, T. D., & Woo, C. (2020) · Nature Protocols · doi:10.1038/s41596-019-0289-5 · Cited by 168 · see citing works
Pate, A., Riley, R. D., Collins, P. G. S., van Smeden, M., CALSTER, B. V., Ensor, J., & Martin, G. P. (2023) · Statistical Methods in Medical Research · doi:10.1177/09622802231151220 · Cited by 172 · see citing works
Self-citationDoes lower-limb asymmetry increase injury risk in sport? A systematic review
Helme, M., Tee, J. C., Emmonds, S., & Low, C. (2021) · Physical Therapy in Sport · doi:10.1016/j.ptsp.2021.03.001 · Cited by 157 · see citing works
Peng, J., Jury, E. C., Dönnes, P., & Ciurtin, C. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.720694 · Cited by 162 · see citing works
Riley, R. D., Snell, K. I. E., Martin, G. P., Whittle, R., Archer, L., Sperrin, M., & Collins, G. S. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.12.005 · Cited by 142 · see citing works
Self-citationSufriyana, H., Husnayain, A., Chen, Y.-L., Kuo, C.-Y., Singh, O., Yeh, T.-Y., Wu, Y., & Su, E. C. (2020) · JMIR Medical Informatics · doi:10.2196/16503 · Cited by 145 · see citing works
Chalasani, N., Ramasubramanian, T. S., Bhattacharya, A., Olson, M. C., Edwards, D. K., V, Roberts, L. R., Kisiel, J. B., Reddy, K. R., Lidgard, G. P., Johnson, S. C., et al. (2020) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2020.08.065 · Cited by 133 · see citing works
Van Calster, B., van Smeden, M., De Cock, B., & Steyerberg, E. W. (2020) · Statistical Methods in Medical Research · doi:10.1177/0962280220921415 · Cited by 120 · see citing works
Snell, K. I. E., Archer, L., Ensor, J., Bonnett, L. J., Debray, T. P. A., Phillips, B., Collins, P. G. S., & Riley, R. D. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.02.011 · Cited by 125 · see citing works
Self-citationWang, Y., Lang, J., Zuo, J. Z., Dong, Y., Hu, Z., Xu, X., Zhang, Y., Wang, Q., Yang, L., Wong, S. T. C., et al. (2022) · European Radiology · doi:10.1007/s00330-022-08887-0 · Cited by 108 · see citing works
Gil, S. D. S., Filho, W. J., Shinjo, S. K., Ferriolli, E., Busse, A. L., Avelino‐Silva, T. J., Longobardi, I., Júnior, G. N. D. O., Swinton, P. A., Gualano, B., et al. (2021) · Journal of Cachexia Sarcopenia and Muscle · doi:10.1002/jcsm.12789 · Cited by 104 · see citing works
Dhiman, P., Ma, J., Qi, C., Bullock, G. S., Sergeant, J. C., Riley, R. D., & Collins, P. G. S. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-02008-1 · Cited by 99 · see citing works
Self-citationClift, A. K., Dodwell, D., Lord, S., Petrou, S., Brady, M., Collins, G. S., & Hippisley-Cox, J. (2023) · BMJ · doi:10.1136/bmj-2022-073800 · Cited by 97 · see citing works
Analyzing Activity and Injury: Lessons Learned from the Acute:Chronic Workload Ratio
Wang, C., Vargas, J. T., Stokes, T., Steele, R. J., & Shrier, I. (2020) · Sports Medicine · doi:10.1007/s40279-020-01280-1 · Cited by 103 · see citing works
Sisk, R., Sperrin, M., Peek, N., van Smeden, M., & Martin, G. P. (2023) · Statistical Methods in Medical Research · doi:10.1177/09622802231165001 · Cited by 99 · see citing works
Overview of clinical prediction models
Chen, L. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2019.11.121 · Cited by 94 · see citing works
Ren, F., Wei, J., Chen, Q., Hu, M., Yu, L., Mi, J., Zhou, X., Qin, D., Wu, J., & Wu, A. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.030 · Cited by 97 · see citing works
Prognostic prediction models for chronic postsurgical pain in adults: a systematic review
Papadomanolakis‐Pakis, N., Uhrbrand, P., Haroutounian, S., & Nikolajsen, L. (2021) · Pain · doi:10.1097/j.pain.0000000000002261 · Cited by 89 · see citing works
Importance of sample size on the quality and utility of AI-based prediction models for healthcare
Riley, R. D., Ensor, J., Snell, K. I. E., Archer, L., Whittle, R., Dhiman, P., Alderman, J. E., Liu, X., Kirton, L., Manson-Whitton, J., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.01.013 · Cited by 93 · see citing works
Self-citationNaser, M. Z., Kodur, V. K. R., Thai, H., Hawileh, R. A., Abdalla, J. A., & Degtyarev, V. V. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.102977 · Cited by 84 · see citing works
Collings, T., Diamond, L. E., BARRETT, R. S., Timmins, R. G., HICKEY, J. T., MOULIN, W. S. D., WILLIAMS, M. D., BEERWORTH, K. A., & Bourne, M. N. (2022) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002908 · Cited by 80 · see citing works
Hoogland, J., IntHout, J., Belias, M., Rovers, M. M., Riley, R. D., Harrell, F. E., Moons, K. G. M., Debray, T. P. A., & Reitsma, J. B. (2021) · Statistics in Medicine · doi:10.1002/sim.9154 · Cited by 79 · see citing works
Self-citationMa, J., Dhiman, P., Qi, C., Bullock, G. S., van Smeden, M., Riley, R. D., & Collins, P. G. S. (2023) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2023.07.017 · Cited by 78 · see citing works
Self-citationSwinckels, L., Bennis, F. C., Ziesemer, K. A., Scheerman, J. F. M., Bijwaard, H., de Keijzer, A., & Bruers, J. J. M. (2024) · Journal of Medical Internet Research · doi:10.2196/48320 · Cited by 77 · see citing works
Allenbach, Y., Saadoun, D., Maalouf, G., Vieira, M., Hellio, A., Boddaert, J., Gros, H. L., Salem, J., Resche‐Rigon, M., Cherifa, M., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0240711 · Cited by 79 · see citing works
Haines, K. J., Hibbert, E., McPeake, J. M., Anderson, B. J., Bienvenu, O. J., Andrews, A., Brummel, N. E., Ferrante, L. E., Hopkins, R. O., Hough, C. L., et al. (2020) · Critical Care Medicine · doi:10.1097/ccm.0000000000004659 · Cited by 75 · see citing works
Zigiotto, L., Annicchiarico, L., Corsini, F., Vitali, L., Falchi, R., Dalpiaz, C., Rozzanigo, U., Barbareschi, M., Avesani, P., Papagno, C., et al. (2020) · Journal of Neuro-Oncology · doi:10.1007/s11060-020-03494-9 · Cited by 61 · see citing works
Boyd, L. N. C., Ali, M., Leeflang, M., Treglia, G., de Vries, R., Large, T. Y. S. L., Besselink, M. G., Giovannetti, E., van Laarhoven, H. W. M., & Geert Kazemier, M. (2022) · EClinicalMedicine · doi:10.1016/j.eclinm.2022.101747 · Cited by 70 · see citing works
Riley, R. D., CALSTER, B. V., & Collins, P. G. S. (2020) · Statistics in Medicine · doi:10.1002/sim.8806 · Cited by 70 · see citing works
Self-citationRadiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education
Wu, Y.-J., Wu, F., Yang, S.-C., Tang, E., & Liang, C.-H. (2022) · Diagnostics · doi:10.3390/diagnostics12051064 · Cited by 69 · see citing works
Training Load and Its Role in Injury Prevention, Part 2: Conceptual and Methodologic Pitfalls
Impellizzeri, F. M., McCall, A., Ward, P., Bornn, L., & Coutts, A. J. (2020) · Journal of Athletic Training · doi:10.4085/1062-6050-501-19 · Cited by 67 · see citing works
Wang, L., Gao, Z., Li, C., Sun, L., Li, J., Yu, J., & Meng, X. Z. (2021) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2021.04.047 · Cited by 67 · see citing works
Venema, E., Wessler, B. S., Paulus, J. K., Salah, R., Raman, G., Leung, L. Y., Koethe, B. C., Nelson, J., Park, J. G., Klaveren, D. V., et al. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.06.017 · Cited by 65 · see citing works
Machine learning techniques for mortality prediction in emergency departments: a systematic review
Naemi, A., Schmidt, T., Mansourvar, M., Naghavi‐Behzad, M., Ebrahimi, A., & Wiil, U. K. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-052663 · Cited by 67 · see citing works
Missing data was handled inconsistently in UK prediction models: a review of method used
Tsvetanova, A., Sperrin, M., Peek, N. B., Buchan, I. E., Hyland, S. L., & Martin, G. P. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.09.008 · Cited by 63 · see citing works
van Royen, F. S.-A., Moons, K. G. M., Geersing, G., & van Smeden, M. (2022) · European Respiratory Journal · doi:10.1183/13993003.00250-2022 · Cited by 60 · see citing works
Self-citationDebray, T. P. A., Collins, P. G. S., Riley, R. D., Snell, K. I. E., CALSTER, B. V., Reitsma, J. B., & Moons, K. G. M. (2023) · BMJ · doi:10.1136/bmj-2022-071058 · Cited by 62 · see citing works
Self-citationClinical prediction models and the multiverse of madness
Riley, R. D., Pate, A., Dhiman, P., Archer, L., Martin, G. P., & Collins, P. G. S. (2023) · BMC Medicine · doi:10.1186/s12916-023-03212-y · Cited by 63 · see citing works
Self-citationCommon statistical concepts in the supervised Machine Learning arena
Rashidi, H. H., Albahra, S., Robertson, S., Tran, N. K., & Hu, B. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1130229 · Cited by 59 · see citing works
Dodsworth, B. T., Reeve, K. A., Falco, L., Hueting, T. A., Sadeghirad, B., Mbuagbaw, L. C. E., Goettel, N., & Gelsomino, N. S. (2023) · Age and Ageing · doi:10.1093/ageing/afad086 · Cited by 59 · see citing works
Halligan, S., Menu, Y., & Mallett, S. (2021) · European Radiology · doi:10.1007/s00330-021-07971-1 · Cited by 50 · see citing works
Bullock, G. S., Hughes, T., Arundale, A. J. H., Ward, P., Collins, P. G. S., & Kluzek, S. (2022) · Sports Medicine · doi:10.1007/s40279-022-01655-6 · Cited by 57 · see citing works
Priyadharshini, M., Balaji, D., Bhuvaneswari, V., Rajeshkumar, L., Rangappa, S. M., & Siengchin, S. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09775-y · Cited by 58 · see citing works
Estimation of required sample size for external validation of risk models for binary outcomes
Pavlou, M., Chen, Q., Omar, R., Seaman, S. R., Steyerberg, E. W., White, I. R., & Ambler, G. (2021) · Statistical Methods in Medical Research · doi:10.1177/09622802211007522 · Cited by 58 · see citing works
McNamara, M. E., Zisser, M., Beevers, C. G., & Shumake, J. (2022) · Behaviour Research and Therapy · doi:10.1016/j.brat.2022.104086 · Cited by 56 · see citing works
Developing more generalizable prediction models from pooled studies and large clustered data sets
de Jong, V. M. T., Moons, K. G. M., Eijkemans, M. J. C., Riley, R. D., & Debray, T. P. A. (2021) · Statistics in Medicine · doi:10.1002/sim.8981 · Cited by 56 · see citing works
Self-citationBias in Odds Ratios From Logistic Regression Methods With Sparse Data Sets
Gosho, M., Ohigashi, T., Nagashima, K., Ito, Y., & Maruo, K. (2021) · Journal of Epidemiology · doi:10.2188/jea.je20210089 · Cited by 64 · see citing works
A review of machine learning with small and limited data
Naser, M. Z. (2026) · Journal Of Big Data · doi:10.1186/s40537-025-01346-9 · Cited by 80 · see citing works
Kamel, R., Negm, S., Youssef, A., Bianchini, L., Brunelli, E., Pilu, G., Soliman, M., & Nicolaides, K. H. (2021) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2020.12.1212 · Cited by 51 · see citing works
Kallinikos, J. (2010) · Palgrave Macmillan UK eBooks · doi:10.1057/9780230295148 · Cited by 130 · see citing works
Prediction of Shoulder Stiffness After Arthroscopic Rotator Cuff Repair
Audigé, L., Aghlmandi, S., Grobet, C. E., Stojanov, T., Müller, A. M., Felsch, Q. T. M., Gleich, J., Flury, M. P., & Scheibel, M. T. (2021) · The American Journal of Sports Medicine · doi:10.1177/03635465211028980 · Cited by 49 · see citing works
Bamford, T., Easter, C., Montgomery, S., Smith, R., Dhillon‐Smith, R. K., Barrie, A., Campbell, A., & Coomarasamy, A. (2023) · Human Reproduction · doi:10.1093/humrep/dead034 · Cited by 50 · see citing works
Perry, B. I., Osimo, E. F., Upthegrove, R., Mallikarjun, P. K., Yorke, J., Štochl, J., Pérez, J., Zammit, S., Howes, O. D., Jones, P. B., et al. (2021) · The Lancet Psychiatry · doi:10.1016/s2215-0366(21)00114-0 · Cited by 48 · see citing works
Hertzberg, L. B., Miller, T. R., Byerly, S. I., Rebello, E., Flood, P. D., Malinzak, E. B., Doyle, C. A., Pease, S., Rock-Klotz, J. A., Kraus, M. B., et al. (2021) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000005676 · Cited by 50 · see citing works
Ullmann, T., Heinze, G., Hafermann, L., Wallisch, C., Dunkler, D., & initiative, F. T. O. T. S. (2024) · PLoS ONE · doi:10.1371/journal.pone.0308543 · Cited by 50 · see citing works
Hughes, A. E., Sovio, U., Gaccioli, F., Cook, E., Jones, D. S., & Smith, G. C. S. (2019) · Placenta · doi:10.1016/j.placenta.2019.04.005 · Cited by 48 · see citing works
Baeza-Delgado, C., Alberich, L. C., Sierra, J. M. C., Veiga-Canuto, D., Heras, B. M. D. L., Raza, B., & Martí‐Bonmatí, L. (2022) · European Radiology Experimental · doi:10.1186/s41747-022-00276-y · Cited by 45 · see citing works
Zhang, J., Yang, K., Wang, C., Gu, W., Li, X.-Q., Fu, S., Song, G., Wang, J., Wu, C., Zhu, H., et al. (2023) · Journal of sport and health science/Journal of Sport and Health Science · doi:10.1016/j.jshs.2023.03.005 · Cited by 45 · see citing works
Clark, T. W., Brendish, N. J., Poole, S., Naidu, V. V., Mansbridge, C., Norton, N., Wheeler, H., Presland, L., & Ewings, S. M. (2020) · Journal of Infection · doi:10.1016/j.jinf.2020.06.051 · Cited by 45 · see citing works
Clinical Prediction Models in Sports Medicine: A Guide for Clinicians and Researchers
Bullock, G. S., Hughes, T., Sergeant, J. C., Callaghan, M. J., Riley, R. D., & Collins, P. G. S. (2021) · Journal of Orthopaedic and Sports Physical Therapy · doi:10.2519/jospt.2021.10697 · Cited by 45 · see citing works
Self-citationWhen and how to use data from randomised trials to develop or validate prognostic models
Pajouheshnia, R., Groenwold, R. H. H., Peelen, L. M., Reitsma, J. B., & Moons, K. G. M. (2019) · BMJ · doi:10.1136/bmj.l2154 · Cited by 42 · see citing works
Self-citationde Jong, Y., Ramspek, C. L., van der Endt, V. H. W., Rookmaaker, M. B., Blankestijn, P. J., Vernooij, R. W. M., Verhaar, M. C., Bos, W. J. W., Dekker, F. W., Ocak, G., et al. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.03.015 · Cited by 43 · see citing works
Crawford, B. J., Sourki, R., Khayyam, H., & Milani, A. S. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103510 · Cited by 41 · see citing works
Martin, G. P., Riley, R. D., Collins, P. G. S., & Sperrin, M. (2021) · Statistical Methods in Medical Research · doi:10.1177/09622802211046388 · Cited by 40 · see citing works
Self-citationLemoine, É., Dallaire, F., Yadav, R., Agarwal, R., Kadoury, S., Trudel, D., Guiot, M., Petrecca, K., & Leblond, F. (2019) · The Analyst · doi:10.1039/c9an01144g · Cited by 41 · see citing works
Pogam, M. L., Seematter‐Bagnoud, L., Niemi, T., Assouline, D., Gross, N., Trächsel, B., Rousson, V., Peytremann‐Bridevaux, I., Burnand, B., & Santos‐Eggimann, B. (2022) · EClinicalMedicine · doi:10.1016/j.eclinm.2021.101260 · Cited by 40 · see citing works
Alvarado‐Bolaños, A., Ayan, D. P., Khaw, A. V., Mai, L. M., Mandzia, J. L., Bogiatzi, C., Mrkobrada, M., Bullrich, M. B., Fleming, L. A., Lippert, C., et al. (2023) · Stroke · doi:10.1161/strokeaha.123.043672 · Cited by 40 · see citing works
Adaptive sample size determination for the development of clinical prediction models
Christodoulou, E., van Smeden, M., Edlinger, M., Timmerman, D., Wanitschek, M. M., Steyerberg, E. W., & CALSTER, B. V. (2021) · Diagnostic and Prognostic Research · doi:10.1186/s41512-021-00096-5 · Cited by 39 · see citing works
Booth, S., Riley, R. D., Ensor, J., Lambert, P. C., & Rutherford, M. J. (2020) · International Journal of Epidemiology · doi:10.1093/ije/dyaa030 · Cited by 39 · see citing works
Self-citationStock, S. J., Horne, M., Bruijn, M. M. C., White, H., Boyd, K., Heggie, R., Wotherspoon, L. M., Aucott, L. S., Morris, R. K., Dorling, J. S., et al. (2021) · PLoS Medicine · doi:10.1371/journal.pmed.1003686 · Cited by 41 · see citing works
Self-citationQu, X., Zhang, C., Houser, S. H., Zhang, J., Zou, J., Zhang, W., & Zhang, Q. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107221 · Cited by 38 · see citing works
Bronchopulmonary dysplasia prediction models: a systematic review and meta-analysis with validation
Kwok, T. C., Batey, N., Luu, K. L., Prayle, A., & Sharkey, D. (2023) · Pediatric Research · doi:10.1038/s41390-022-02451-8 · Cited by 42 · see citing works
Large-Scale Proteomics Improve Prediction of Chronic Kidney Disease in People With Diabetes
Ye, Z., Zhang, Y., Zhang, Y., Yang, S., He, P., Liu, M., Zhou, C., Gan, X., Huang, Y., Xiang, H., et al. (2024) · Diabetes Care · doi:10.2337/dc24-0290 · Cited by 38 · see citing works
Prognostic models for predicting relapse or recurrence of major depressive disorder in adults
Moriarty, A. S., Meader, N., Snell, K. I. E., Riley, R. D., Paton, L. W., Chew-Graham, C. A., Gilbody, S. M., Churchill, R. C., Phillips, R. S., Ali, S., et al. (2021) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013491.pub2 · Cited by 35 · see citing works
Self-citationPawel, S., Kook, L., & Reeve, K. A. (2023) · Biometrical Journal · doi:10.1002/bimj.202200091 · Cited by 37 · see citing works
Reeve, K. A., Ön, B. I., Havla, J. B., Burns, J., Gosteli, M., Alabsawi, A., Alayash, Z., Götschi, A., Seibold, H., Mansmann, U., et al. (2023) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013606.pub2 · Cited by 36 · see citing works
- Authors
- Riley, R. D., Snell, K. I., Ensor, J., Burke, D. L., Harrell Jr, F. E., Moons, K. G., & Collins, G. S.
- Affiliations
- Keele University (GB)
- Department of Biostatistics Vanderbilt University School of Medicine Nashville Tennessee
- University Medical Center Utrecht (NL)
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK
- Corresponding author
- Riley, Richard D
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2018-10-24
- Volume
- 38
- Issue
- 7
- Pages
- 1276-1296
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- PMC
- PMC6519266
- OpenAlex
- W2898559532
- Cited by
- 1,409
- References
- 61
- Citations by year
- 2026: 483 · 2025: 250 · 2024: 186 · 2023: 134 · 2022: 120 · 2021: 123 · 2020: 93 · 2019: 16 · 2018: 3
- FWCI
- 49.17
- Type
- journal article
- Language
- en
- Update notices
- correction (2019-10-29)
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Statistics in Medicine (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Keele Research Repository (Keele University) (submittedVersion, cc-by)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, cc-by-nc)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, other-oa)
- Funders
- National Institute for Health Research
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (9120.8004, 918.10.615)
- National Center for Advancing Translational Sciences (UL1 TR002243)
- 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
- minimum sample size, binary outcomes, time-to-event outcomes, overfitting, shrinkage factor, prognostic model, diagnostic model, Chagas disease, recurrent venous thromboembolism
- MeSH terms
- Computer Simulation, Humans, Regression Analysis, Time, Multivariate Analysis, Sample Size
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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.
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