A new concordance measure for risk prediction models in external validation settings

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 · see citing works

Proportional hazards tests and diagnostics based on weighted residuals

GRAMBSCH, P. M., & THERNEAU, T. M. (1994) · Biometrika · doi:10.1093/biomet/81.3.515 · Cited by 5,885 · see citing works

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 · see citing works

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 · see citing works

Coronary artery bypass graft surgery versus percutaneous coronary intervention in patients with three-vessel disease and left main coronary disease: 5-year follow-up of the randomised, clinical SYNTAX trial

Mohr, F. W., Morice, M., Kappetein, A. P., Feldman, T. E., Ståhle, E., Colombo, A., Mack, M. J., Holmes, D. R., Morel, M.-A., Dyck, N. V., et al. (2013) · The Lancet · doi:10.1016/s0140-6736(13)60141-5 · Cited by 1,788 · see citing works

On the C‐statistics for evaluating overall adequacy of risk prediction procedures with censored survival data

Uno, H., Cai, T., Pencina, M. J., D'Agostino, R. B., & Wei, L. J. (2011) · Statistics in Medicine · doi:10.1002/sim.4154 · Cited by 1,535 · see citing works

Clinical Prediction Models

Steyerberg, E. W. (2009) · Statistics for Biology and Health · doi:10.1007/978-0-387-77244-8 · Cited by 1,491 · see citing works

Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation

Pencina, M. J., & D’Agostino, R. B. (2004) · Statistics in Medicine · doi:10.1002/sim.1802 · Cited by 1,446 · see citing works

Predicting Outcome after Traumatic Brain Injury: Development and International Validation of Prognostic Scores Based on Admission Characteristics

Steyerberg, E. W., Mushkudiani, N. A., Perel, P., Butcher, I., Lü, J., McHugh, G. S., Murray, G. D., Marmarou, A., Roberts, I., Habbema, J. D. F., et al. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050165 · Cited by 1,437 · see citing works

External validation of a Cox prognostic model: principles and methods

Royston, P. J., & Altman, D. G. (2013) · BMC Medical Research Methodology · doi:10.1186/1471-2288-13-33 · Cited by 911 · see citing works

Anatomical and clinical characteristics to guide decision making between coronary artery bypass surgery and percutaneous coronary intervention for individual patients: development and validation of SYNTAX score II

Farooq, V., Klaveren, D. V., Steyerberg, E. W., Meliga, E., Vergouwe, Y., Chieffo, A., Kappetein, A. P., Colombo, A., Holmes, D. R., Mack, M. J., et al. (2013) · The Lancet · doi:10.1016/s0140-6736(13)60108-7 · Cited by 837 · see citing works

Concordance probability and discriminatory power in proportional hazards regression

Gönen, M., & Heller, G. (2005) · Biometrika · doi:10.1093/biomet/92.4.965 · Cited by 685 · see citing works

A multicenter trial on the efficacy of using tirilazad mesylate in cases of head injury

Marshall, L. F., Maas, A. I. R., Marshall, S. B., Bricolo, A., Fearnside, M. R., Iannotti, F., Klauber, M. R., Lagarrigue, J., Lobato, R., Persson, L., et al. (1998) · Journal of neurosurgery · doi:10.3171/jns.1998.89.4.0519 · Cited by 336 · see citing works

Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable

Austin, P. C., & Steyerberg, E. W. (2012) · BMC Medical Research Methodology · doi:10.1186/1471-2288-12-82 · Cited by 270 · see citing works

External Validity of Risk Models: Use of Benchmark Values to Disentangle a Case-Mix Effect From Incorrect Coefficients

Vergouwe, Y., Moons, K. G. M., & Steyerberg, E. W. (2010) · American Journal of Epidemiology · doi:10.1093/aje/kwq223 · Cited by 243 · see citing works

Measures of explained variation for survival data

Korn, E. L., & Simon, R. M. (1990) · Statistics in Medicine · doi:10.1002/sim.4780090503 · Cited by 194 · see citing works

Regional differences in patient characteristics, case management, and outcomes in traumatic brain injury: experience from the tirilazad trials

Hukkelhoven, C. W. P. M., Steyerberg, E. W., Farace, E., Habbema, J. D. F., Marshall, L. F., & Maas, A. I. R. (2002) · Journal of neurosurgery · doi:10.3171/jns.2002.97.3.0549 · Cited by 112 · see citing works

Long-term safety and efficacy of sirolimus-eluting stents versus bare-metal stents in real world clinical practice in Japan

Kimura, T., Morimoto, T., Furukawa, Y., Nakagawa, Y., Kadota, K., Iwabuchi, M., Shizuta, S., Shiomi, H., Tada, T., Tazaki, J., et al. (2011) · Cardiovascular Intervention and Therapeutics · doi:10.1007/s12928-011-0065-0 · Cited by 108 · see citing works

Summary measure of discrimination in survival models based on cumulative/dynamic time-dependent ROC curves

Lambert, J., & Chevret, S. (2014) · Statistical Methods in Medical Research · doi:10.1177/0962280213515571 · Cited by 103 · see citing works

Predictive Performance of SYNTAX Score II in Patients With Left Main and Multivessel Coronary Artery Disease

Campos, C. M., Klaveren, D. V., Iqbal, J., Onuma, Y., Zhang, Y., García‐García, H. M., Morel, M., Farooq, V., Shiomi, H., Furukawa, Y., et al. (2014) · Circulation Journal · doi:10.1253/circj.cj-14-0204 · Cited by 73 · see citing works

Nonparametric Partial Correlation

Quade, D. (1974) · doi:10.1007/978-1-349-02473-5_13 · Cited by 39 · see citing works

A Measure of Explained Variation for Event History Data

Stare, J., Perme, M. P., & Henderson, R. (2010) · Biometrics · doi:10.1111/j.1541-0420.2010.01526.x · Cited by 22 · see citing works

Interpretation of concordance measures for clustered data

Klaveren, D. V., Steyerberg, E. W., & Vergouwe, Y. (2014) · Statistics in Medicine · doi:10.1002/sim.5928 · Cited by 10 · see citing works

survC1: C-Statistics for Risk Prediction Models with Censored Survival Data

Uno, H. (2011) · doi:10.32614/cran.package.survc1 · Cited by 2 · see citing works

PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration

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

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

A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes

Debray, T. P., Damen, J. A., Riley, R. D., Snell, K., Reitsma, J. B., Hooft, L., Collins, G. S., & Moons, K. G. (2019) · Statistical Methods in Medical Research · doi:10.1177/0962280218785504 · Cited by 278 · see citing works

Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals

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

Methodological standards for the development and evaluation of clinical prediction rules: a review of the literature

Cowley, L., Farewell, D. M., Maguire, S., & Kemp, A. (2019) · Diagnostic and Prognostic Research · doi:10.1186/s41512-019-0060-y · Cited by 265 · see citing works

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration

Kent, D. M., Klaveren, D. V., Paulus, J. K., D’Agostino, R. B., Goodman, S. N., Hayward, R. A., Ioannidis, J. P. A., Patrick‐Lake, B., Morton, S. C., Pencina, M. J., et al. (2019) · Annals of Internal Medicine · doi:10.7326/m18-3668 · Cited by 177 · see citing works

Assessing Performance and Clinical Usefulness in Prediction Models With Survival Outcomes: Practical Guidance for Cox Proportional Hazards Models

McLernon, D. J., Giardiello, D., CALSTER, B. V., Wynants, L., van Geloven, N., van Smeden, M., Therneau, T. M., Steyerberg, E. W., Initiative, T. G. 6. A. 8. O. T. S., McLernon, D. J., et al. (2022) · Annals of Internal Medicine · doi:10.7326/m22-0844 · Cited by 167 · see citing works

Meta-analysis of prediction model performance across multiple studies: Which scale helps ensure between-study normality for the C -statistic and calibration measures?

Snell, K. I. E., Ensor, J., Debray, T. P. A., Moons, K. G. M., & Riley, R. D. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280217705678 · Cited by 130 · see citing works

Kidney Failure Prediction Models: A Comprehensive External Validation Study in Patients with Advanced CKD

Ramspek, C. L., Evans, M., Wanner, C., Drechsler, C., Chesnaye, N. C., Szymczak, M., Krajewska, M., Torino, C., Porto, G., Hayward, S., et al. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2020071077 · Cited by 91 · see citing works

External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature

Wessler, B. S., Nelson, J., Park, J. G., McGinnes, H., Gulati, G., Brazil, R., Van Calster, B., van Klaveren, D., Venema, E., Steyerberg, E., et al. (2021) · Circulation: Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.121.007858 · Cited by 83 · see citing works

Prediction of Global Functional Outcome and Post-Concussive Symptoms after Mild Traumatic Brain Injury: External Validation of Prognostic Models in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Study

Mikolić, A., Polinder, S., Steyerberg, E. W., Helmrich, I. R., Giacino, J. T., Maas, A. I. R., van der Naalt, J., Voormolen, D. C., von Steinbüchel, N., Wilson, L., et al. (2020) · Journal of Neurotrauma · doi:10.1089/neu.2020.7074 · Cited by 70 · see citing works

Large-scale validation of the prediction model risk of bias assessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination

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

Generalizability of Cardiovascular Disease Clinical Prediction Models: 158 Independent External Validations of 104 Unique Models

Gulati, G., Upshaw, J., Wessler, B. S., Brazil, R. J., Nelson, J., Klaveren, D. V., Lundquist, C. M., Park, J. G., McGinnes, H., Steyerberg, E. W., et al. (2022) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.121.008487 · Cited by 60 · see citing works

Untapped potential of multicenter studies: a review of cardiovascular risk prediction models revealed inappropriate analyses and wide variation in reporting

Wynants, L., Kent, D. M., Timmerman, D., Lundquist, C. M., & CALSTER, B. V. (2019) · Diagnostic and Prognostic Research · doi:10.1186/s41512-019-0046-9 · Cited by 45 · see citing works

Predicting disability progression in multiple sclerosis: Insights from advanced statistical modeling

Pellegrini, F., Copetti, M., Sormani, M. P., Bovis, F., de Moor, C., Debray, T. P. A., & Kieseier, B. C. (2019) · Multiple Sclerosis Journal · doi:10.1177/1352458519887343 · Cited by 33 · see citing works

Model-Based ROC Curve: Examining the Effect of Case Mix and Model Calibration on the ROC Plot

Sadatsafavi, M., Saha‐Chaudhuri, P., & Petkau, J. (2021) · Medical Decision Making · doi:10.1177/0272989x211050909 · Cited by 34 · see citing works

Transporting a Prediction Model for Use in a New Target Population

Steingrimsson, J. A., Gatsonis, C. G., Li, B., & Dahabreh, I. J. (2022) · American Journal of Epidemiology · doi:10.1093/aje/kwac128 · Cited by 31 · see citing works

Identifying unreliable predictions in clinical risk models

Myers, P. D., Ng, K., Severson, K., Kartoun, U., Dai, W., Huang, W., Anderson, F. A., & Stultz, C. M. (2020) · npj Digital Medicine · doi:10.1038/s41746-019-0209-7 · Cited by 30 · see citing works

COVID outcome prediction in the emergency department (COPE): using retrospective Dutch hospital data to develop simple and valid models for predicting mortality and need for intensive care unit admission in patients who present at the emergency department with suspected COVID-19

Klaveren, D. V., Rekkas, A., Alsma, J., Verdonschot, R. J. C. G., Koning, D., Kamps, M. J. A., Dormans, T. P. J., Stassen, R. C., Weijer, S., Arnold, K.-S., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-051468 · Cited by 18 · see citing works

Does poor methodological quality of prediction modeling studies translate to poor model performance? An illustration in traumatic brain injury

Helmrich, I. R., Mikolić, A., Kent, D. M., Lingsma, H. F., Wynants, L., Steyerberg, E. W., & Klaveren, D. V. (2022) · Diagnostic and Prognostic Research · doi:10.1186/s41512-022-00122-0 · Cited by 17 · see citing works

Evaluating individualized treatment effect predictions: A model‐based perspective on discrimination and calibration assessment

Hoogland, J., Efthimiou, O., Nguyen, T., & Debray, T. P. A. (2024) · Statistics in Medicine · doi:10.1002/sim.10186 · Cited by 17 · see citing works

Evaluating disease prediction models using a cohort whose covariate distribution differs from that of the target population

Powers, S., McGuire, V. M., Bernstein, L., Canchola, A. J., & Whittemore, A. S. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280217723945 · Cited by 12 · see citing works

RCT versus real-world cohorts: Differences in patient characteristics drive associations with outcome after EVT

Quandt, F., Meißner, N., Wölfer, T. A., Flottmann, F., Deb‐Chatterji, M., Kellert, L., Fiehler, J., Goyal, M., Saver, J. L., Gerloff, C. P., et al. (2022) · European Stroke Journal · doi:10.1177/23969873221142642 · Cited by 11 · see citing works

Pitfalls of single-study external validation illustrated with a model predicting functional outcome after aneurysmal subarachnoid hemorrhage

de Winkel, J., Maas, C. C. H. M., Roozenbeek, B., Klaveren, D. V., & Lingsma, H. F. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02280-9 · Cited by 10 · see citing works

External validation of prognostic models predicting outcome after chronic subdural hematoma

Holl, D. C., Mikolić, A., Blaauw, J., Lodewijkx, R., Foppen, M., Jellema, K., Gaag, N. A. V. D., den Hertog, H. M., Jacobs, B., van der Naalt, J., et al. (2022) · Acta Neurochirurgica · doi:10.1007/s00701-022-05216-8 · Cited by 9 · see citing works

Personalized decision-making for aneurysm treatment of aneurysmal subarachnoid hemorrhage: development and validation of a clinical prediction tool

de Winkel, J., Roozenbeek, B., Dijkland, S. A., Dammers, R., van Doormaal, P., van der Jagt, M., Klaveren, D. V., Dippel, D. W. J., & Lingsma, H. F. (2024) · BMC Neurology · doi:10.1186/s12883-024-03546-x · Cited by 6 · see citing works

Prognostic models for COVID-19 needed updating to warrant transportability over time and space

van Klaveren, D., Zanos, T. P., Nelson, J., Levy, T. J., Park, J. G., Retel Helmrich, I. R. A., Rietjens, J. A. C., Basile, M. J., Hajizadeh, N., Lingsma, H. F., et al. (2022) · BMC Medicine · doi:10.1186/s12916-022-02651-3 · Cited by 5 · see citing works

The calibrated model-based concordance improved assessment of discriminative ability in patient clusters of limited sample size

Klaveren, D. V., Steyerberg, E. W., Gönen, M., & Vergouwe, Y. (2019) · Diagnostic and Prognostic Research · doi:10.1186/s41512-019-0055-8 · Cited by 5 · see citing works

Propensity‐based standardization to enhance the validation and interpretation of prediction model discrimination for a target population

de Jong, V. M. T., Hoogland, J., Moons, K. G. M., Riley, R. D., Nguyen, T., & Debray, T. P. A. (2023) · Statistics in Medicine · doi:10.1002/sim.9817 · Cited by 5 · see citing works

Development and External Validation of a Prediction Model for Patients with Varicose Veins Suitable for Isolated Ambulatory Phlebectomy

Scheerders, E. R. Y., Klaveren, D. V., Malskat, W. S. J., van Rijn, M. J. E., van der Velden, S. K., Nijsten, T. E. C., & van den Bos, R. R. (2024) · European Journal of Vascular and Endovascular Surgery · doi:10.1016/j.ejvs.2024.05.001 · Cited by 4 · see citing works

Harnessing multi-source data for individualized care in Hodgkin Lymphoma

Parsons, S. K., Rodday, A. M., Upshaw, J. N., Scharman, C. D., Zhu, C., Cao, Y., Tiger, Y. K., Maurer, M. J., & Evens, A. M. (2024) · Blood Reviews · doi:10.1016/j.blre.2024.101170 · Cited by 4 · see citing works

Extending prediction models for use in a new target population with failure time outcomes

Steingrimsson, J. A. (2022) · Biostatistics · doi:10.1093/biostatistics/kxac011 · Cited by 3 · see citing works

External Validation of the Post-Concussion Symptoms Rule for Predicting Mild Traumatic Brain Injury Outcome

Mikolić, A., Brasher, P. M. A., Brubacher, J. R., Panenka, W. J., Scheuermeyer, F. X., Archambault, P. M., Khazei, A., & Silverberg, N. D. (2024) · Journal of Neurotrauma · doi:10.1089/neu.2023.0484 · Cited by 2 · see citing works

External Validation of the ARISE Prediction Models for Aneurysmal Rebleeding After Aneurysmal Subarachnoid Hemorrhage

Mijderwijk, H.-J., de Winkel, J., Nieboer, D., Abdelaal, A., Germans, M. R., Karadağ, C., Cornelius, J. F., Sorteberg, A. G., Roozenbeek, B., Lingsma, H. F., et al. (2024) · Neurosurgery · doi:10.1227/neu.0000000000003275 · Cited by 2 · see citing works

Performance of Heart Failure Clinical Prediction Models: A Systematic External Validation StudyPreprint

Upshaw, J. N., Nelson, J., Koethe, B. C., Park, J. G., McGinnes, H. L., Wessler, B. S., Konstam, M. A., Udelson, J. E., CALSTER, B. V., Klaveren, D. V., et al. (2021) · medRxiv · doi:10.1101/2021.01.31.21250875 · Cited by 2 · see citing works

Assessing Algorithm Fairness Requires Adjustment for Risk Distribution Differences: Re-considering the Equal Opportunity CriterionPreprint

Hegarty, S. E., Linn, K. A., Zhang, H., Teeple, S., Albert, P. S., Parikh, R. B., Courtright, K. R., Kent, D. M., & Chen, J. (2025) · medRxiv · doi:10.1101/2025.01.31.25321489 · Cited by 1 · see citing works

The Generalizability of Clinical Prediction Models for Patients with Acute Coronary Syndromes: Results from Independent External ValidationsPreprint

Wessler, B. S., Nelson, J., Park, J. G., McGinnes, H. L., Upshaw, J. N., CALSTER, B. V., Klaveren, D. V., Steyerberg, E. W., & Kent, D. M. (2021) · medRxiv · doi:10.1101/2021.01.21.21250234

Clinical Prediction Models for Primary Prevention of Cardiovascular Disease: Validity in Independent CohortsPreprint

Gulati, G., Brazil, R., Nelson, J., Klaveren, D. V., Lundquist, C. M., Park, J. G., McGinnes, H. L., Steyerberg, E. W., CALSTER, B. V., Wessler, B. S., et al. (2021) · medRxiv · doi:10.1101/2021.01.31.21250871

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The SweDen risk score- predicting death 1-year after myocardial infarctionPreprint

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A Temporal Validation Study of Diagnostic Prediction Models for the Screening of Elevated Low-Density and Non-High-Density Lipoprotein Cholesterol

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Additional file 1 of Personalized decision-making for aneurysm treatment of aneurysmal subarachnoid hemorrhage: development and validation of a clinical prediction tool

de Winkel, J., Roozenbeek, B., Dijkland, S. A., Dammers, R., van Doormaal, P., van der Jagt, M., van Klaveren, D., Dippel, D. W. J., & Lingsma, H. F. (2024) · Figshare · doi:10.6084/m9.figshare.26681041

Additional file 1 of Prognostic models for COVID-19 needed updating to warrant transportability over time and space

van Klaveren, D., Zanos, T. P., Nelson, J., Levy, T. J., Park, J. G., Retel Helmrich, I. R. A., Rietjens, J. A. C., Basile, M. J., Hajizadeh, N., Lingsma, H. F., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21622824

Additional file 1 of Does poor methodological quality of prediction modeling studies translate to poor model performance? An illustration in traumatic brain injury

Helmrich, I. R., Mikolić, A., Kent, D. M., Lingsma, H. F., Wynants, L., Steyerberg, E. W., & van Klaveren, D. (2022) · Figshare · doi:10.6084/m9.figshare.19711396

Additional file 1 of Pitfalls of single-study external validation illustrated with a model predicting functional outcome after aneurysmal subarachnoid hemorrhage

de Winkel, J., Maas, C. C. H. M., Roozenbeek, B., van Klaveren, D., & Lingsma, H. F. (2024) · Figshare · doi:10.6084/m9.figshare.26758733

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Review and evaluation of performance measures for survival prediction models in external validation settings

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A Proper Concordance Index for Time-Varying RiskPreprint

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