The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Evaluating Prediction Rules for t -Year Survivors With Censored Regression Models
Open accessAbstract
Suppose that we are interested in establishing simple but reliable rules for predicting future t-year survivors through censored regression models. In this article we present inference procedures for evaluating such binary classification rules based on various prediction precision measures quantified by the overall misclassification rate, sensitivity and specificity, and positive and negative predictive values. Specifically, under various working models, we derive consistent estimators for the above measures through substitution and cross-validation estimation procedures. Furthermore, we provide large-sample approximations to the distributions of these nonsmooth estimators without assuming that the working model is correctly specified. Confidence intervals, for example, for the difference of the precision measures between two competing rules can then be constructed. All of the proposals are illustrated with real examples, and their finite-sample properties are evaluated through a simulation study.
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“Uno and colleagues' inverse probability of censoring weights method can account for censoring.”
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [82]
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Blanche, P., Dartigues, J., & Jacqmin‐Gadda, H. (2013) · Statistics in Medicine · doi:10.1002/sim.5958 · Cited by 1,917
Loi, S., Drubay, D., Adams, S., Pruneri, G., Francis, P. A., Lacroix-Triki, M., Joensuu, H., Dieci, M. V., Badve, S., Demaria, S., et al. (2019) · Journal of Clinical Oncology · doi:10.1200/jco.18.01010 · Cited by 893
Time-dependent ROC curve analysis in medical research: current methods and applications
Kamarudin, A. N., Cox, T. F., & Kolamunnage‐Dona, R. (2017) · BMC Medical Research Methodology · doi:10.1186/s12874-017-0332-6 · Cited by 839
MELD 3.0: The Model for End-Stage Liver Disease Updated for the Modern Era
Kim, W. R., Mannalithara, A., Heimbach, J. K., Kamath, P. S., Asrani, S. K., Biggins, S. W., Wood, N. L., Gentry, S. E., & Kwong, A. J. (2021) · Gastroenterology · doi:10.1053/j.gastro.2021.08.050 · Cited by 654
Developing 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
Metadata
- Authors
- Uno, H., Cai, T., Tian, L., & Wei, L. J.
- Affiliations
- Northwestern University (US)
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2007-06
- Volume
- 102
- Issue
- 478
- Pages
- 527-537
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2082174016
- Cited by
- 413
- References
- 48
- Citations by year
- 2026: 32 · 2025: 37 · 2024: 33 · 2023: 35 · 2022: 46 · 2021: 43 · 2020: 50 · 2019: 20 · 2018: 14 · 2017: 20 · 2016: 18 · 2015: 10 · 2014: 10 · 2013: 15 · 2012: 7
- FWCI
- 4.74
- Type
- journal article
- Language
- en
- Open access
- green
- biostats.bepress.com
- Locations
- Journal of the American Statistical Association (publishedVersion)
- Collection of Biostatistics Research Archive (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Keywords
- prediction rules, binary classification, misclassification rate, sensitivity and specificity, predictive value, consistent estimators, cross-validation, large sample approximation, confidence intervals, finite sample properties
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