The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Assessing calibration of prognostic risk scores
Open accessAbstract
Current methods used to assess calibration are limited, particularly in the assessment of prognostic models. Methods for testing and visualizing calibration (e.g. the Hosmer–Lemeshow test and calibration slope) have been well thought out in the binary regression setting. However, extension of these methods to Cox models is less well known and could be improved. We describe a model-based framework for the assessment of calibration in the binary setting that provides natural extensions to the survival data setting. We show that Poisson regression models can be used to easily assess calibration in prognostic models. In addition, we show that a calibration test suggested for use in survival data has poor performance. Finally, we apply these methods to the problem of external validation of a risk score developed for the general population when assessed in a special patient population (i.e. patients with particular comorbidities, such as rheumatoid arthritis).
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study
D’Agostino, R. B., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M., & Kannel, W. B. (2008) · Circulation · doi:10.1161/circulationaha.107.699579 · Cited by 7,491
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
Two Further Applications of a Model for Binary Regression
Cox, D. R. (1958) · Biometrika · doi:10.2307/2333203 · Cited by 557
Validation of probabilistic predictions
Miller, M. E., Langefeld, C. D., Tierney, W. M., Hui, S. L., & McDonald, C. J. (1993) · Medical Decision Making · doi:10.1177/0272989x9301300107 · Cited by 129
Peek, N., Arts, D. G. T., Bosman, R. J., van der Voort, P. H. J., & de Keizer, N. F. (2007) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2006.08.011 · Cited by 92 · cited 1×
“Limitations of the Hosmer–Lemeshow test are well known and include its dependence on arbitrary groupings of patients, poor power in small data sets as well as the fact that it only results in a p value.(1,9) A calibration plot is sometimes described as a visual representation of the Hosmer–Lemeshow test, because it ca…”
Method
Cited by
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
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
Inherent trade-offs in the fair determination of risk scores
Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.itcs.2017.43 · Cited by 943
“A first basic goal in this literature is that the probability estimates provided by the algorithm should be well-calibrated: if the algorithm identifies a set of people as having a probability z of constituting positive instances, then approximately a z fraction of this set should indeed be positive instances [8, 14].”
BackgroundDeveloping clinical prediction models: a step-by-step guide
Efthimiou, O., Seo, M., Chalkou, K., Debray, T., Egger, M., & Salanti, G. (2024) · BMJ · doi:10.1136/bmj-2023-078276 · Cited by 544
Tests of calibration and goodness‐of‐fit in the survival setting
Demler, O. V., Paynter, N. P., & Cook, N. R. (2015) · Statistics in Medicine · doi:10.1002/sim.6428 · Cited by 287
Metadata
- Authors
- Crowson, C. S., Atkinson, E. J., & Therneau, T. M.
- Venue
- Statistical Methods in Medical Research
- Publisher
- SAGE Publications
- Published
- 2016-07-11
- Volume
- 25
- Issue
- 4
- Pages
- 1692-1706
- ISSN
- 0962-2802 (ISSN-L)
- 1477-0334
- OpenAlex
- W2161009717
- Cited by
- 289
- References
- 5
- FWCI
- 6.59
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Assessing calibration of prognostic risk scores. https://app.cochat.ai/papers/10.1177/0962280213497434
- The paper
- Crowson, C. S., Atkinson, E. J., & Therneau, T. M. (2016). Assessing calibration of prognostic risk scores. Statistical Methods in Medical Research. https://doi.org/10.1177/0962280213497434
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
