Cited by 195Statistical Methods in EpidemiologyMeta-analysis and systematic reviewsInflammatory Biomarkers in Disease Prognosis

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Reporting performance of prognostic models in cancer: a review.

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Mallett, Susan, Royston, Patrick, Waters, Rachel, Dutton, Susan, Altman, Douglas G·2010·BMC Medicine

DOI 10.1186/1741-7015-8-21PMC PMC2857810

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Abstract

BACKGROUND: Appropriate choice and use of prognostic models in clinical practice require the use of good methods for both model development, and for developing prognostic indices and risk groups from the models. In order to assess reliability and generalizability for use, models need to have been validated and measures of model performance reported. We reviewed published articles to assess the methods and reporting used to develop and evaluate performance of prognostic indices and risk groups from prognostic models. METHODS: We developed a systematic search string and identified articles from PubMed. Forty-seven articles were included that satisfied the following inclusion criteria: published in 2005; aiming to predict patient outcome; presenting new prognostic models in cancer with outcome time to an event and including a combination of at least two separate variables; and analysing data using multivariable analysis suitable for time to event data. RESULTS: In 47 studies, Cox models were used in 94% (44), but the coefficients or hazard ratios for the variables in the final model were reported in only 72% (34). The reproducibility of the derived model was assessed in only 11% (5) of the articles. A prognostic index was developed from the model in 81% (38) of the articles, but researchers derived the prognostic index from the final prognostic model in only 34% (13) of the studies; different coefficients or variables from those in the final model were used in 50% (19) of models and the methods used were unclear in 16% (6) of the articles. Methods used to derive prognostic groups were also poor, with researchers not reporting the methods used in 39% (14 of 36) of the studies and data derived methods likely to bias estimates of differences between risk groups being used in 28% (10) of the studies. Validation of their models was reported in only 34% (16) of the studies. In 15 studies validation used data from the same population and in five studies from a different population. Including reports of validation with external data from publications up to four years following model development, external validation was attempted for only 21% (10) of models. Insufficient information was provided on the performance of models in terms of discrimination and calibration. CONCLUSIONS: Many published prognostic models have been developed using poor methods and many with poor reporting, both of which compromise the reliability and clinical relevance of models, prognostic indices and risk groups derived from them.

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Regression Modeling Strategies
Harrell, F. E. (2001) · Springer series in statistics · doi:10.1007/978-1-4757-3462-1 · Cited by 9,270
Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond
Pencina, M. J., D' Agostino, R. B., D' Agostino, R. B., & Vasan, R. S. (2008) · Statistics in Medicine · doi:10.1002/sim.2929 · Cited by 6,493
Decision curve analysis: a novel method for evaluating prediction models
Vickers, A. J., & Elkin, E. B. (2006) · Medical Decision Making · doi:10.1177/0272989x06295361 · Cited by 6,341
Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating
Steyerberg, E. W. (2019) · Statistics for Biology and Health · doi:10.1007/978-3-030-16399-0 · Cited by 3,281
Dichotomizing continuous predictors in multiple regression: a bad idea
Royston, P., Altman, D. G., & Sauerbrei, W. (2006) · Statistics in Medicine · doi:10.1002/sim.2331 · Cited by 2,312
Hepatogastroenterology
Xu, X., Yuan, G., Li, Q., Shan, S.-L., & Jiang, S. (2012) · Hepatogastroenterology · doi:10.5754/hge12404 · Cited by 1,825
Clinical Prediction Models
Steyerberg, E. W. (2009) · Statistics for Biology and Health · doi:10.1007/978-0-387-77244-8 · Cited by 1,491
Prognosis and prognostic research: validating a prognostic model
Altman, D. G., Vergouwe, Y., Royston, P., & Moons, K. G. M. (2009) · BMJ · doi:10.1136/bmj.b605 · Cited by 1,436
Prognosis and prognostic research: what, why, and how?
Moons, K. G. M., Royston, P., Vergouwe, Y., Grobbee, D. E., & Altman, D. G. (2009) · BMJ · doi:10.1136/bmj.b375 · Cited by 1,337
Comments on ‘Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond’ by M. J. Pencina, R. B. D'Agostino Sr, R. B. D'Agostino Jr, R. S. Vasan, Statistics in Medicine (DOI: 10.1002/sim.2929)
Greenland, P. (2007) · Statistics in Medicine · doi:10.1002/sim.2976 · Cited by 1,300

Show all 113 references

Research works printed but not linked: 1

Steyerberg EW: Clinical prediction models. 2008, New York: Springer

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Rose, J., Homa, L., Kong, C. Y., Cooper, G. S., Kattan, M. W., Ermlich, B., Meyers, J. P., Primrose, J. M., Pugh, S. A., Shinkins, B., et al. (2019) · Cancer Causes & Control · doi:10.1007/s10552-019-01187-x · Cited by 3
Calibration of Prognostic Risk Scores
CALSTER, B. V., & Steyerberg, E. W. (2018) · Wiley eBooks · doi:10.1002/9781118445112.stat08078 · Cited by 3
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Cheng, D. T.-Y., Ramos‐Cejudo, J., Tuck, D. P., Elbers, D. C., Brophy, M. T., Do, N., & Fillmore, N. R. (2019) · Seminars in Oncology · doi:10.1053/j.seminoncol.2019.09.006 · Cited by 4
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Moons, K. G. M., Altman, D. G., Reitsma, J. B., loannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, P. G. S. (2023) · Вопросы современной педиатрии · doi:10.15690/vsp.v22i2.2557 · Cited by 1
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Hendrickson, M. R., & Balzer, B. L. (2011) · Springer eBooks · doi:10.1007/978-1-4419-1030-1_7 · Cited by 1
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Maskerine, C. (2017) · uO Research (University of Ottawa) · doi:10.20381/ruor-20165
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Zhai, T. (2020) · doi:10.33612/diss.111448998
Learning from big imaging data to predict radiotherapy treatment outcomes and side-effects
Zhenwei, S. (2020) · doi:10.26481/dis.20200908zs
Stabilizing Calibration of Clinical Prediction Models in Non-Stationary Environments: Methods Supporting Data-Driven Model Updating
Davis, S. E. (2019)
References
Riley, R. D., Tierney, J. F., & Stewart, L. A. (2021) · Wiley eBooks · doi:10.1002/9781119333784.refs5
References
(2013) · Elsevier eBooks · doi:10.1016/b978-0-12-404576-7.09978-0
Special Considerations in Big Data Analysis
Berman, J. J. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-815609-4.00014-5
A Framework for Evaluating Analytic Hyperparameters
Prakash, S., Brzoska, A., & Amin, S. (2022) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2022.v14i1.3199
Performance of the Matsumiya scoring system in cervical cancer patients with bone metastasis: an external validation study
Wongyikul, P., Wongchanudom, S., Lumkul, L., Isaradech, N., Phanphaisarn, A., Phinyo, P., & Pruksakorn, D. (2022) · International Journal of Clinical Oncology · doi:10.1007/s10147-022-02273-y
Preoperative predictors of survival of patients with hepatocellular carcinoma undergoing liver resection beyond the updated Barcelona Clinic Liver Cancer criteriaPreprint
Yen, Y., Liu, Y., Li, W., Lin, C., Yong, C., Wang, C., & Lin, C. (2023) · Research Square · doi:10.21203/rs.3.rs-2671229/v1
Differences in adverse outcomes across race and ethnicity among Veterans with similar predicted risks of an overdose or suicide-related event
Garrido, M. M., Legler, A., Strombotne, K. L., & Frakt, A. B. (2023) · Pain Medicine · doi:10.1093/pm/pnad129
Modified neoadjuvant rectal score as a novel prognostic model for rectal cancer patients who underwent chemoradiotherapy
Kawai, K., Ozaki, K., Nakano, D., Dejima, A., Ise, I., Nakamori, S., Kato, H., Natsume, S., Takao, M., Yamaguchi, T., et al. (2024) · International Journal of Clinical Oncology · doi:10.1007/s10147-024-02520-4
Special Considerations in Big Data Analysis
Berman, J. J. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-404576-7.00010-1
Leveraging neural networks for robust survival estimation in cox proportional hazards model with censored data
Moradi, N., Habibirad, A., & Panahi, H. (2024) · Communications in Statistics Case Studies Data Analysis and Applications · doi:10.1080/23737484.2024.2439847
Leveraging two-phase data for improved prediction of survival outcomes with application to nasopharyngeal cancer
Oh, E. J., Ahn, S., Tham, T., & Qian, M. (2025) · Biometrics · doi:10.1093/biomtc/ujaf080
Cardiovascular risk prediction in Central and Eastern Europe and former Soviet Union
Vikhireva, O. (2012) · UCL Discovery (University College London)
Additional file 1 of Prognostic value of patient-reported outcome measures (PROMs) in adults with non-small cell Lung Cancer: a scoping review
Liao, K., Wang, T., Coomber-Moore, J., Wong, D., Gomes, F., Finn, C. F., Sperrin, M., Yorke, J., & van der Veer, S. N. (2022) · Figshare · doi:10.6084/m9.figshare.21366065
Additional file 1 of Machine learning to improve interpretability of clinical, radiological and panel-based genomic data of glioma grade 4 patients undergoing surgical resection
Dal Bo, M., Polano, M., Ius, T., Di Cintio, F., Mondello, A., Manini, I., Pegolo, E., Cesselli, D., Di Loreto, C., Škrap, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26602972
Predictive Modeling for Cancer Prognosis
Vats, P., Baweja, B., Nirmal, S., & Rawat, L. (2025) · Springer eBooks · doi:10.1007/978-981-96-9346-7_9
Evaluation of six clinical prognostic scores in NSCLC patients undergoing first line chemoimmunotherapy
Sun, J., Li, D., Liu, J., Wang, L., Wang, L., Han, J., Zhang, X., Zhou, X., Feng, L., Fan, Z., et al. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1695859
Visual analytics framework for survival analysis and biomarker discovery from gene expression data
Kokošar, J., Turkay, Ç., Ausec, L., Štajdohar, M. R., & Zupan, B. (2026) · PLoS ONE · doi:10.1371/journal.pone.0325399
Predicting Days at Home after Elective Surgery for Gastrointestinal Cancer using HOMEDAYS: Prediction Model Development and Internal Validation
Ribeiro, T., Bondzi‐Simpson, A., Chan, W. C., Mahar, A., Jerath, A., Coburn, N. G., & Hallet, J. I. (2026) · Journal of the American College of Surgeons · doi:10.1097/xcs.0000000000002011

Authors: Mallett, S., Royston, P., Waters, R., Dutton, S., & Altman, D. G.
Affiliations: University of Oxford (GB)
MRC Clinical Trials Unit at UCL (GB)
Corresponding author: Mallett, Susan
ORCID: Mallett, Susan
Royston, Patrick
Dutton, Susan
Altman, Douglas G
Venue: BMC Medicine
Publisher: Springer Science and Business Media LLC
Published: 2010-03-30
Volume: 8
Issue: 1
Pages: 21-21
ISSN: 1741-7015 (ISSN-L)
DOI: 10.1186/1741-7015-8-21
PMC: PMC2857810
OpenAlex: W1988375742
Cited by: 195
References: 113
Citations by year: 2026: 3 · 2025: 6 · 2024: 14 · 2023: 11 · 2022: 17 · 2021: 11 · 2020: 14 · 2019: 12 · 2018: 13 · 2017: 10 · 2016: 16 · 2015: 14 · 2014: 18 · 2013: 11 · 2012: 13
FWCI: 1.59
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
www.springer.com/tdm (unspecified)
Locations: BMC Medicine (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
UCL Discovery (University College London) (submittedVersion)
University of Birmingham Research Portal (University of Birmingham) (submittedVersion, other-oa)
Funders: Cancer Research UK
Medical Research Council (MC_EX_G0800814, U.1228.06.001.00002.01)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Health Sciences › Medicine › Oncology › Inflammatory Biomarkers in Disease Prognosis
Keywords: prognostic model, cancer, reporting quality, prognostic score, risk groups, model validation, external validation, discrimination (statistics), calibration, Cox model, hazard ratio, reproducibility, time to event
MeSH terms: Evaluation Studies as Topic, Humans, Neoplasms, Prognosis, Statistics as Topic

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