The DOI matches its Crossref record.
Several methods to assess improvement in risk prediction models: Extension to survival analysis
Abstract
Risk prediction models have been widely applied for the prediction of long‐term incidence of disease. Several parameters have been identified and estimators developed to quantify the predictive ability of models and to compare new models with traditional models. These estimators have not generally accounted for censoring in the survival data normally available for fitting the models. This paper remedies that problem. The primary parameters considered are net reclassification improvement (NRI) and integrated discrimination improvement (IDI). We have previously similarly considered a primary measure of concordance, area under the ROC curve (AUC), also called the c‐statistic. We also include here consideration of population attributable risk (PAR) and ratio of predicted risk in the top quintile of risk to that in the bottom quintile. We evaluated estimators of these various parameters both with simulation studies and also as applied to a prospective study of coronary heart disease (CHD). Our simulation studies showed that in general our estimators had little bias, and less bias and smaller variances than the traditional estimators. We have applied our methods to assessing improvement in risk prediction for each traditional CHD risk factor compared to a model without that factor. These traditional risk factors are considered valuable, yet when adding any of them to a risk prediction model that has omitted the one factor, the improvement is generally small for any of the parameters. This experience should prepare us to not expect large values of the risk prediction improvement evaluation parameters for any new risk factor to be discovered. Copyright © 2010 John Wiley & Sons, Ltd.
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
Cited by
Mahabadi, A. A., Berg, M. H., Lehmann, N., Kälsch, H., Bauer, M., Kara, K., Dragano, N., Moebus, S., Jöckel, K.-H., Erbel, R., et al. (2013) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2012.11.062 · Cited by 529
Evaluating a New International Risk-Prediction Tool in IgA Nephropathy
Barbour, S. J., Coppo, R., Zhang, H., Liu, Z.-H., Suzuki, Y., Matsuzaki, K., Katafuchi, R., Er, L., Espino-Hernandez, G., Kim, S. J., et al. (2019) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2019.0600 · Cited by 478
Pencina, M. J., D'Agostino, R. B., & Demler, O. V. (2012) · Statistics in Medicine · doi:10.1002/sim.4348 · Cited by 349
Agarwal, S. K., Chambless, L. E., Ballantyne, C. M., Astor, B., Bertoni, A. G., Chang, P. P., Folsom, A. R., He, M., Hoogeveen, R. C., Ni, H., et al. (2012) · Circulation: Heart Failure · doi:10.1161/circheartfailure.111.964841 · Cited by 266
Self-citationExalto, L. G., Biessels, G. J., Karter, A. J., Huang, E. S., Katon, W. J., Minkoff, J. R., & Whitmer, R. A. (2013) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(13)70048-2 · Cited by 244
Palatini, P., Reboldi, G., Beilin, L. J., Casiglia, E., Eguchi, K., Imai, Y., Kario, K., Ohkubo, T., Pierdomenico, S. D., Schwartz, J. E., et al. (2014) · Hypertension · doi:10.1161/hypertensionaha.114.03694 · Cited by 193
Karter, A. J., Warton, E. M., Lipska, K. J., Ralston, J. D., Moffet, H. H., Jackson, G. G., Huang, E. S., & Miller, D. R. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2017.3844 · Cited by 150
Novel genetic markers improve measures of atrial fibrillation risk prediction
Everett, B. M., Cook, N. R., Conen, D., Chasman, D. I., Ridker, P. M., & Albert, C. M. (2013) · European Heart Journal · doi:10.1093/eurheartj/eht033 · Cited by 141
La Rovere, M. T., Pinna, G. D., Maestri, R., Barlera, S., Bernardinangeli, M., Veniani, M., Nicolosi, G. L., Marchioli, R., Tavazzi, L., & for the GISSI-HF Investigators (2012) · European Journal of Heart Failure · doi:10.1093/eurjhf/hfs126 · Cited by 82
Use of reclassification for assessment of improved prediction: an empirical evaluation
Tzoulaki, I., Liberopoulos, G., & Ioannidis, J. P. A. (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr013 · Cited by 70
Filion, K. B., Agarwal, S. K., Ballantyne, C. M., Eberg, M., Hoogeveen, R. C., Huxley, R. R., Loehr, L. R., Nambi, V., Soliman, E. Z., & Alonso, A. (2015) · American Heart Journal · doi:10.1016/j.ahj.2014.10.005 · Cited by 73
Yatsuya, H., Iso, H., Yamagishi, K., Kokubo, Y., Saito, I., Suzuki, K., Sawada, N., Inoue, M., & Tsugane, S. (2013) · Stroke · doi:10.1161/strokeaha.111.677534 · Cited by 56
Austin, P. C., Pencinca, M. J., & Steyerberg, E. W. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280214567141 · Cited by 39
Metadata
- Authors
- Chambless, L. E., Cummiskey, C. P., & Cui, G.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2010-09-08
- Volume
- 30
- Issue
- 1
- Pages
- 22-38
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2083283521
- Cited by
- 194
- References
- 0
- FWCI
- 16.97
- Type
- journal article
- Language
- en
- Open access
- closed
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
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.
