Diagnostic and prognostic prediction models

Applied logistic regression

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CONSORT statement: extension to cluster randomised trials

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Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research

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Prognosis and prognostic research: validating a prognostic model

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Clinical Prediction Rules: Applications and Methodological Standards

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Prognosis and prognostic research: what, why, and how?

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Derivation and validation of a prognostic model for pulmonary embolism

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Risk prediction models: II. External validation, model updating, and impact assessment

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Selection of important variables and determination of functional form for continuous predictors in multivariable model building

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Prognosis and prognostic research: Developing a prognostic model

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Assessing the Generalizability of Prognostic Information

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Clinical prediction rules. A review and suggested modifications of methodological standards

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Prognosis and prognostic research: application and impact of prognostic models in clinical practice

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Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis

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Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker

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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

Importance of events per independent variable in proportional hazards analysis I. Background, goals, and general strategy

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Clinical Prediction Models: A Practical Approach to Development, Validation and Updating

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Translating Clinical Research into Clinical Practice: Impact of Using Prediction Rules To Make Decisions

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Clinical Prediction Rules<subtitle>A Review and Suggested Modifications of Methodological Standards</subtitle>

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Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: a clinical example.

van der Heijden, G. V. D., Donders, A., Stijnen, T., & Moons, K. (2006) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2006.01.015 · Cited by 664 · see citing works

Prognosis research strategy (PROGRESS) 1: A framework for researching clinical outcomes

Hemingway, H., Croft, P. R., Perel, P., Hayden, J. A., Abrams, K. R., Timmis, A., Briggs, A. H., Udumyan, R., Moons, K. G. M., Steyerberg, E. W., et al. (2013) · BMJ · doi:10.1136/bmj.e5595 · Cited by 662 · see citing works

Regression With Missing X's: A Review

Little, R. J. A. (1992) · Journal of the American Statistical Association · doi:10.2307/2290664 · Cited by 647 · see citing works

Reporting and Methods in Clinical Prediction Research: A Systematic Review

Bouwmeester, W., Zuithoff, N. P. A., Mallett, S., Geerlings, M. I., Vergouwe, Y., Steyerberg, E. W., Altman, D. G., & Moons, K. G. M. (2012) · PLoS Medicine · doi:10.1371/journal.pmed.1001221 · Cited by 600 · see citing works

Outpatient versus inpatient treatment for patients with acute pulmonary embolism: an international, open-label, randomised, non-inferiority trial

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Missing covariate data in medical research: To impute is better than to ignore

Janssen, K. J. M., Donders, A. R. T., Harrell, F. E., Vergouwe, Y., Chen, Q., Grobbee, D. E., & Moons, K. G. M. (2010) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2009.12.008 · Cited by 593 · see citing works

Validation, updating and impact of clinical prediction rules: A review

Toll, D. B., Janssen, K. J. M., Vergouwe, Y., & Moons, K. G. M. (2008) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2008.04.008 · Cited by 584 · see citing works

Identifying unprovoked thromboembolism patients at low risk for recurrence who can discontinue anticoagulant therapy

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Validation and updating of predictive logistic regression models: a study on sample size and shrinkage

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Prognosis research strategy (PROGRESS) 4: Stratified medicine research

Hingorani, A. D., van der Windt, D., Riley, R. D., Abrams, K. R., Moons, K. G. M., Steyerberg, E. W., Schroter, S., Sauerbrei, W., Altman, D. G., Hemingway, H., et al. (2013) · BMJ · doi:10.1136/bmj.e5793 · Cited by 514 · see citing works

Risk Assessment of Recurrence in Patients With Unprovoked Deep Vein Thrombosis or Pulmonary Embolism

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Case–Control and Two-Gate Designs in Diagnostic Accuracy Studies

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How should variable selection be performed with multiply imputed data?

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Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

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Advances in Measuring the Effect of Individual Predictors of Cardiovascular Risk: The Role of Reclassification Measures

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Predicting disease recurrence in patients with previous unprovoked venous thromboembolism: a proposed prediction score (DASH)

Tosetto, A., Iorio, A., Marcucci, M., Baglin, T. P., Cushman, M., Eichinger, S., Palareti, G., Poli, D., Tait, R. C., & Douketis, J. D. (2012) · Journal of Thrombosis and Haemostasis · doi:10.1111/j.1538-7836.2012.04735.x · Cited by 439 · see citing works

Updating methods improved the performance of a clinical prediction model in new patients

Janssen, K. J. M., Moons, K. G. M., Kalkman, C. J., Grobbee, D. E., & Vergouwe, Y. (2007) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2007.04.018 · Cited by 381 · see citing works

Statistical methods for assessment of added usefulness of new biomarkers

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Clinical Decision Rules for Excluding Pulmonary Embolism: A Meta-analysis

Lucassen, W. A. M., Geersing, G., Erkens, P. M. G., Reitsma, J. B., Moons, K. G. M., Buller, H. R., & van Weert, H. C. P. M. (2011) · Annals of Internal Medicine · doi:10.7326/0003-4819-155-7-201110040-00007 · Cited by 323 · see citing works

Clinical Epidemiology: Principles, Methods, And Applications For Clinical Research

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Integrating the Predictiveness of a Marker with Its Performance as a Classifier

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Development and validation of a prediction model with missing predictor data: a practical approach

Vergouwe, Y., Royston, P. J., Moons, K. G. M., & Altman, D. G. (2009) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2009.03.017 · Cited by 288 · see citing works

Assessment of Claims of Improved Prediction Beyond the Framingham Risk Score

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Prediction models for the risk of cardiovascular disease in patients with type 2 diabetes: a systematic review

van Dieren, S., Beulens, J. W. J., Kengne, A. P., Peelen, L. M., Rutten, G. E. H. M., Woodward, M., van der Schouw, Y. T., & Moons, K. G. M. (2011) · Heart · doi:10.1136/heartjnl-2011-300734 · Cited by 217 · see citing works

Reporting methods in studies developing prognostic models in cancer: a review

Mallett, S., Royston, P., Dutton, S., Waters, R., & Altman, D. G. (2010) · BMC Medicine · doi:10.1186/1741-7015-8-20 · Cited by 206 · see citing works

Reporting performance of prognostic models in cancer: a review.

Mallett, S., Royston, P., Waters, R., Dutton, S., & Altman, D. G. (2010) · BMC Medicine · doi:10.1186/1741-7015-8-21 · Cited by 195 · see citing works

Limitations of Sensitivity, Specificity, Likelihood Ratio, and Bayesʼ Theorem in Assessing Diagnostic Probabilities: A Clinical Example

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Comparison of the revised Geneva score with the Wells rule for assessing clinical probability of pulmonary embolism

Klok, F. A., KRUISMAN, E., SPAAN, J., Nijkeuter, M., Righini, M., Aujesky, D., Roy, P., Perrier, A., Gal, G. L., & Huisman, M. V. (2008) · Journal of Thrombosis and Haemostasis · doi:10.1111/j.1538-7836.2007.02820.x · Cited by 166 · see citing works

Safe exclusion of pulmonary embolism using the Wells rule and qualitative D-dimer testing in primary care: prospective cohort study

Geersing, G., Erkens, P. M. G., Lucassen, W. A. M., Buller, H. R., Cate, H. T., Hoes, A. W., Moons, K. G. M., Prins, M. H., Oudega, R., van Weert, H. C. P. M., et al. (2012) · BMJ · doi:10.1136/bmj.e6564 · Cited by 155 · see citing works

Dealing with Missing Predictor Values When Applying Clinical Prediction Models

Janssen, K. J. M., Vergouwe, Y., Donders, A. R. T., Harrell, F. E., Chen, Q., Grobbee, D. E., & Moons, K. G. M. (2009) · Clinical Chemistry · doi:10.1373/clinchem.2008.115345 · Cited by 152 · see citing works

Quantifying the Added Value of a Diagnostic Test or Marker

Moons, K. G. M., de Groot, J. A. H., Linnet, K., Reitsma, J. B., & Bossuyt, P. M. M. (2012) · Clinical Chemistry · doi:10.1373/clinchem.2012.182550 · Cited by 152 · see citing works

Ruling out deep venous thrombosis in primary care

Moons, K. G. M., Hoes, A. W., & Oudega, R. (2005) · Thrombosis and Haemostasis · doi:10.1160/th04-12-0829 · Cited by 144 · see citing works

Test Research versus Diagnostic Research

Moons, K. G. M., Biesheuvel, C. J., & Grobbee, D. E. (2004) · Clinical Chemistry · doi:10.1373/clinchem.2003.024752 · Cited by 137 · see citing works

Advantages of the nested case-control design in diagnostic research

Biesheuvel, C. J., Vergouwe, Y., Oudega, R., Hoes, A. W., Grobbee, D. E., & Moons, K. G. M. (2008) · BMC Medical Research Methodology · doi:10.1186/1471-2288-8-48 · Cited by 135 · see citing works

Management studies using a combination of D-dimer test result and clinical probability to rule out venous thromboembolism: a systematic review

ten Cate‐Hoek, A. J., & Prins, M. H. (2005) · Journal of Thrombosis and Haemostasis · doi:10.1111/j.1538-7836.2005.01556.x · Cited by 133 · see citing works

A simple method to adjust clinical prediction models to local circumstances

Janssen, K. J. M., Vergouwe, Y., Kalkman, C. J., Grobbee, D. E., & Moons, K. G. M. (2009) · Canadian Journal of Anesthesia/Journal canadien d anesthésie · doi:10.1007/s12630-009-9041-x · Cited by 126 · see citing works

Risk of recurrent venous thromboembolism after stopping treatment in cohort studies: recommendation for acceptable rates and standardized reporting

Kearon, C., Iorio, A., & Palareti, G. (2010) · Journal of Thrombosis and Haemostasis · doi:10.1111/j.1538-7836.2010.03991.x · Cited by 124 · see citing works

Bias arising from missing data in predictive models.

Gorelick, M. H. (2006) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2004.11.029 · Cited by 107 · see citing works

Strategies for comparing treatments on a binary response with multi-centre data

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Risk Prediction Measures for Case-Cohort and Nested Case-Control Designs: An Application to Cardiovascular Disease

Ganna, A., Reilly, M., Fairé, U. D., Pedersen, N., Magnusson, P. K. E., & Ingelsson, E. (2012) · American Journal of Epidemiology · doi:10.1093/aje/kwr374 · Cited by 90 · see citing works

Redundancy of Single Diagnostic Test Evaluation

Moons, K. G. M., van Es, G.-A., Michel, B. C., Buller, H. R., Habbema, J. D. F., & Grobbee, D. E. (1999) · Epidemiology · doi:10.1097/00001648-199905000-00015 · Cited by 89 · see citing works

Prediction models for clustered data: comparison of a random intercept and standard regression model

Bouwmeester, W., Twisk, J. W. R., Kappen, T. H., van Klei, W. A., Moons, K. G. M., & Vergouwe, Y. (2013) · BMC Medical Research Methodology · doi:10.1186/1471-2288-13-19 · Cited by 87 · see citing works

Diagnostic studies as multivariable, prediction research

Moons, K. G. M., & Grobbee, D. E. (2002) · Journal of Epidemiology & Community Health · doi:10.1136/jech.56.5.337 · Cited by 75 · see citing works

Cost‐effectiveness of ruling out deep venous thrombosis in primary care versus care as usual

ten Cate‐Hoek, A. J., Toll, D. B., Buller, H. R., Hoes, A. W., Moons, K. G. M., Oudega, R., Stoffers, J., van der Velde, E. F., van Weert, H. C. P. M., Prins, M. H., et al. (2009) · Journal of Thrombosis and Haemostasis · doi:10.1111/j.1538-7836.2009.03627.x · Cited by 72 · see citing works

Criteria for Scientific Evaluation of Novel Markers: A Perspective

Moons, K. G. M. (2010) · Clinical Chemistry · doi:10.1373/clinchem.2009.134155 · Cited by 70 · see citing works

Decision analysis to complete diagnostic research by closing the gap between test characteristics and cost-effectiveness

Schaafsma, J. D., van der Graaf, Y., Rinkel, G. J. E., & Buskens, E. (2009) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2009.01.008 · Cited by 55 · see citing works

Multiple imputation to correct for partial verification bias revisited

de Groot, J. A. H., Janssen, K. J. M., Zwinderman, A. H., Moons, K. G. M., & Reitsma, J. B. (2008) · Statistics in Medicine · doi:10.1002/sim.3410 · Cited by 52 · see citing works

A new diagnostic rule for deep vein thrombosis: safety and efficiency in clinically relevant subgroups

Toll, D. B., Oudega, R., Vergouwe, Y., Moons, K. G. M., & Hoes, A. W. (2007) · Family Practice · doi:10.1093/fampra/cmm075 · Cited by 22 · see citing works

Long-term health benefits and costs of measurement of carotid intima–media thickness in prevention of coronary heart disease

den Ruijter, H. M., Vaartjes, I. C. H., Sutton-Tyrrell, K., Bots, M. L., & Koffijberg, H. (2013) · Journal of Hypertension · doi:10.1097/hjh.0b013e32835e8ee5 · Cited by 15 · see citing works

Assessing the potential for bias in meta-analysis due to selective reporting of subgroup analyses within studies

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What do we mean by validating a prognostic model?

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Development of a risk prediction model for Barrett's esophagus in an Australian population

Ireland, C. J., Fielder, A. L., Thompson, S. K., Laws, T. A., Watson, D. I., & Esterman, A. (2017) · Diseases of the Esophagus · doi:10.1093/dote/dox033 · Cited by 15 · see citing works

Optimization of the diagnostic management of clinically suspected pulmonary embolism in hospitalized patients

van der Hulle, T., den Exter, P. L., Mos, I. C. M., Kamphuisen, P. W., Hovens, M. M. C., Kruip, M. J. H. A., van Es, J., Cate, H. T., Huisman, M. V., & Klok, F. A. (2014) · British Journal of Haematology · doi:10.1111/bjh.13090 · Cited by 14 · see citing works

Improved Landmark Dynamic Prediction Model to Assess Cardiovascular Disease Risk in On‐Treatment Blood Pressure Patients: A Simulation Study and Post Hoc Analysis on SPRINT Data

Sayadi, M., Zare, N., Attar, A., & Ayatollahi, S. M. T. (2020) · BioMed Research International · doi:10.1155/2020/2905167 · Cited by 14 · see citing works

Prediction models of diabetes complications: a scoping review

Ndjaboué, R., Ngueta, G., Rochefort-Brihay, C., Delorme, S., Guay, D., Ivers, N. M., Shah, B. R., Straus, S. E., Yu, C. H. Y., Comeau, S., et al. (2022) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2021-217793 · Cited by 13 · see citing works

Prediction of in-hospital bleeding in acutely ill medical patients: External validation of the IMPROVE bleeding risk score

Villiger, R., Julliard, P., Farhoumand, P. D., Choffat, D., Tritschler, T., Stalder, O., Rossel, J., Aujesky, D., Méan, M., & Baumgartner, C. (2023) · Thrombosis Research · doi:10.1016/j.thromres.2023.08.003 · Cited by 13 · see citing works

Why do authors derive new cardiovascular clinical prediction rules in the presence of existing rules? A mixed methods study

Ban, J.-W., Wallace, E., Stevens, R., & Perera, R. (2017) · PLoS ONE · doi:10.1371/journal.pone.0179102 · Cited by 13 · see citing works

A prediction model for early systemic recurrence in breast cancer using a molecular diagnostic analysis of sentinel lymph nodes: A large‐scale, multicenter cohort study

Osako, T., Matsuura, M., Yotsumoto, D., Takayama, S., Kaneko, K., Takahashi, M., Shimazu, K., Yoshidome, K., Kuraoka, K., Itakura, M., et al. (2022) · Cancer · doi:10.1002/cncr.34144 · Cited by 13 · see citing works

Moving beyond silos: How do we provide distributed personalized medicine to pregnant women everywhere at scale? Insights from PRE‐EMPT

von Dadelszen, P., Magee, L. A., Payne, B. A., Dunsmuir, D. T., Drebit, S. K., Dumont, G. A., Miller, S., Norman, J. E., Pyne‐Mercier, L., Shennan, A. H., et al. (2015) · International Journal of Gynecology & Obstetrics · doi:10.1016/j.ijgo.2015.02.008 · Cited by 14 · see citing works

Classification versus Prediction of Mortality Risk using the SIRS and qSOFA Scores in Patients with Infection Transported by Paramedics

Lane, D. J., Lin, S., & Scales, D. C. (2019) · Prehospital Emergency Care · doi:10.1080/10903127.2019.1624901 · Cited by 13 · see citing works

Biomarkers to the Rescue in a Lung Nodule Epidemic

Massion, P. P. (2014) · Journal of Clinical Oncology · doi:10.1200/jco.2013.54.0047 · Cited by 13 · see citing works

A framework for evaluating predictive models

Tan, Y., Saffari, S. E., & Tan, N. C. (2022) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2022.08.005 · Cited by 12 · see citing works

Integrating economic considerations into cutpoint selection may help align clinical decision support toward value-based healthcare

Parsons, R., Blythe, R. D., Cramb, S. M., & McPhail, S. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad042 · Cited by 14 · see citing works

PREDICT: model for prediction of survival in localized prostate cancer

Kerkmeijer, L. G. W., Monninkhof, E. M., van Oort, I. M., van der Poel, H. G., Meerleer, G. D., & van Vulpen, M. (2015) · World Journal of Urology · doi:10.1007/s00345-015-1691-4 · Cited by 12 · see citing works

Developing and Validating a Clinical Warfarin Dose‐Initiation Model for Black‐African Patients in South Africa and Uganda

Asiimwe, I. G., Waitt, C. J., Sekaggya‐Wiltshire, C., Hutchinson, C., Okello, E., Zhang, E. J., Semakula, J. R., Mouton, J. P., Cohen, K., Blockman, M., et al. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2128 · Cited by 12 · see citing works

Development and internal validation of the Comprehensive ALPPS Preoperative Risk Assessment (CAPRA) score: is the patient suitable for Associating Liver Partition and Portal vein ligation for Staged hepatectomy (ALPPS)?

Capobianco, I., Oldhafer, K. J., Fard-Aghaie, M. H., Robles-Campos, R., Brusadín, R., Petrowsky, H., Linecker, M., Mehrabi, A., Hoffmann, K., Li, J., et al. (2022) · HepatoBiliary Surgery and Nutrition · doi:10.21037/hbsn-21-396 · Cited by 12 · see citing works

Prognostic Models in Medicine

Luchinin, A. S. (2023) · Clinical oncohematology · doi:10.21320/2500-2139-2023-16-1-27-36 · Cited by 11 · see citing works

Development and internal validation of a predictive risk model for anxiety after completion of treatment for early stage breast cancer

Harris, J., Purssell, E., Cornelius, V., Ream, E., Jones, A., & Armes, J. (2020) · Journal of Patient-Reported Outcomes · doi:10.1186/s41687-020-00267-w · Cited by 8 · see citing works

Differentiating PSP from MSA using MR planimetric measurements: a systematic review and meta-analysis

Heim, B., Krismer, F., & Seppi, K. (2021) · Journal of Neural Transmission · doi:10.1007/s00702-021-02362-8 · Cited by 11 · see citing works

Technology Solutions for Nurse Leaders

Clancy, T. R. (2020) · Nursing Administration Quarterly · doi:10.1097/naq.0000000000000439 · Cited by 10 · see citing works

Rational development and application of biomarkers in the field of autoimmunity: A conceptual framework guiding clinicians and researchers

Kolev, M., Horn, M. P., Semmo, N., & Nagler, M. (2022) · Journal of Translational Autoimmunity · doi:10.1016/j.jtauto.2022.100151 · Cited by 9 · see citing works

Development of a diagnostic predictive model for determining child stunting in Malawi: a comparative analysis of variable selection approaches

Mkungudza, J., Twabi, H. S., & Manda, S. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02283-6 · Cited by 9 · see citing works

Systematic review of prognostic models for recurrent venous thromboembolism (VTE) post-treatment of first unprovoked VTE

Ensor, J., Riley, R. D., Moore, D., Snell, K. I. E., Bayliss, S., & Fitzmaurice, D. (2016) · BMJ Open · doi:10.1136/bmjopen-2016-011190 · Cited by 84 · see citing works

Predicting venous thromboembolism among hospitalized adults: a protocol for development and validation of an implementable real-time prognostic model

Domenico, H. J., Tillman, B. F., Just, S. L., Ko, Y., Mixon, A. S., Weitkamp, A. O., Schildcrout, J. S., Walsh, C. G., Ortel, T. L., & French, B. C. (2025) · Diagnostic and Prognostic Research · doi:10.1186/s41512-025-00205-8 · Cited by 2 · see citing works

Introduction to Clinical Prediction Models

Iwagami, M., & Matsui, H. (2022) · Annals of Clinical Epidemiology · doi:10.37737/ace.22010 · Cited by 31 · see citing works

Predicting the risk of venous thromboembolism in critically ill patients (PROVE-IT): a model development and validation study

Rivrud, S. C. S., Heijkoop, È. R. H., Hulshof, L., van den Oever, N. C. G., Wetterslev, M., Perner, A., Keus, F., Meijer, K., & Eck, R. J. (2025) · Journal of Thrombosis and Haemostasis · doi:10.1016/j.jtha.2025.06.026 · Cited by 3 · see citing works

Risk of recurrence after a first unprovoked venous thromboembolism: external validation of the Vienna Prediction Model with pooled individual patient data

Marcucci, M., Iorio, A., Douketis, J. D., Eichinger, S., Tosetto, A., Baglin, T. P., Cushman, M., Palareti, G., Poli, D., Tait, R. C., et al. (2015) · Journal of Thrombosis and Haemostasis · doi:10.1111/jth.12871 · Cited by 72 · see citing works